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A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI (2302.05284v3)

Published 10 Feb 2023 in cs.HC, cs.AI, and cs.CY

Abstract: As AI continues to advance rapidly, it becomes increasingly important to consider AI's ethical and societal implications. In this paper, we present a bottom-up mapping of the current state of research at the intersection of Human-Centered AI, Ethical, and Responsible AI (HCER-AI) by thematically reviewing and analyzing 164 research papers from leading conferences in ethical, social, and human factors of AI: AIES, CHI, CSCW, and FAccT. The ongoing research in HCER-AI places emphasis on governance, fairness, and explainability. These conferences, however, concentrate on specific themes rather than encompassing all aspects. While AIES has fewer papers on HCER-AI, it emphasizes governance and rarely publishes papers about privacy, security, and human flourishing. FAccT publishes more on governance and lacks papers on privacy, security, and human flourishing. CHI and CSCW, as more established conferences, have a broader research portfolio. We find that the current emphasis on governance and fairness in AI research may not adequately address the potential unforeseen and unknown implications of AI. Therefore, we recommend that future research should expand its scope and diversify resources to prepare for these potential consequences. This could involve exploring additional areas such as privacy, security, human flourishing, and explainability.

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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John Prior, Florian Evéquoz, and Adrien Depeursinge. 2023. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581506 Vigil-Hayes et al. (2017) Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. 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Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. 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(2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. 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(2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Michalis Vitos, Julia Altenbuchner, Matthias Stevens, Gillian Conquest, Jerome Lewis, and Muki Haklay. 2017. Supporting Collaboration with Non-Literate Forest Communities in the Congo-Basin. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998242 Waldman (2021) Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. 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Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. 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ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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(2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Michalis Vitos, Julia Altenbuchner, Matthias Stevens, Gillian Conquest, Jerome Lewis, and Muki Haklay. 2017. Supporting Collaboration with Non-Literate Forest Communities in the Congo-Basin. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998242 Waldman (2021) Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. 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(2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. 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Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John Prior, Florian Evéquoz, and Adrien Depeursinge. 2023. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581506 Vigil-Hayes et al. (2017) Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3501909 Vitos et al. (2017) Michalis Vitos, Julia Altenbuchner, Matthias Stevens, Gillian Conquest, Jerome Lewis, and Muki Haklay. 2017. Supporting Collaboration with Non-Literate Forest Communities in the Congo-Basin. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998242 Waldman (2021) Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. 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Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). 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(2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. 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(2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. 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BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. 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Issue 3. Valencia et al. (2023) Stephanie Valencia, Richard Cave, Krystal Kallarackal, Katie Seaver, Michael Terry, and Shaun K. Kane. 2023. “The Less I Type, the Better”: How AI Language Models Can Enhance or Impede Communication for AAC Users. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581560 van Berkel et al. (2023) Niels van Berkel, Zhanna Sarsenbayeva, and Jorge Goncalves. 2023. The methodology of studying fairness perceptions in Artificial Intelligence: Contrasting CHI and FAccT. International Journal of Human-Computer Studies (2023). Varanasi and Goyal (2023) Rama Adithya Varanasi and Nitesh Goyal. 2023. “It is Currently Hodgepodge”: Examining AI/ML Practitioners’ Challenges during Co-Production of Responsible AI Values. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580903 Verma et al. (2023) Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John Prior, Florian Evéquoz, and Adrien Depeursinge. 2023. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581506 Vigil-Hayes et al. (2017) Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3501909 Vitos et al. (2017) Michalis Vitos, Julia Altenbuchner, Matthias Stevens, Gillian Conquest, Jerome Lewis, and Muki Haklay. 2017. Supporting Collaboration with Non-Literate Forest Communities in the Congo-Basin. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998242 Waldman (2021) Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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Varanasi and Goyal (2023) Rama Adithya Varanasi and Nitesh Goyal. 2023. “It is Currently Hodgepodge”: Examining AI/ML Practitioners’ Challenges during Co-Production of Responsible AI Values. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580903 Verma et al. (2023) Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John Prior, Florian Evéquoz, and Adrien Depeursinge. 2023. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581506 Vigil-Hayes et al. (2017) Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. 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Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). 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(2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3501909 Vitos et al. (2017) Michalis Vitos, Julia Altenbuchner, Matthias Stevens, Gillian Conquest, Jerome Lewis, and Muki Haklay. 2017. Supporting Collaboration with Non-Literate Forest Communities in the Congo-Basin. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998242 Waldman (2021) Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. 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(2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). 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(2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John Prior, Florian Evéquoz, and Adrien Depeursinge. 2023. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581506 Vigil-Hayes et al. (2017) Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3501909 Vitos et al. (2017) Michalis Vitos, Julia Altenbuchner, Matthias Stevens, Gillian Conquest, Jerome Lewis, and Muki Haklay. 2017. Supporting Collaboration with Non-Literate Forest Communities in the Congo-Basin. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998242 Waldman (2021) Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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(2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). 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(2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. 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Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. 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ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. 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ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. 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Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). 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(2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. 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In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. 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Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). 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(2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. 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(2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. 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Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John Prior, Florian Evéquoz, and Adrien Depeursinge. 2023. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581506 Vigil-Hayes et al. (2017) Morgan Vigil-Hayes, Marisa Duarte, Nicholet Deschine Parkhurst, and Elizabeth Belding. 2017. #Indigenous: Tracking the Connective Actions of Native American Advocates on Twitter. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998194 Viswanathan et al. (2022) Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3501909 Vitos et al. (2017) Michalis Vitos, Julia Altenbuchner, Matthias Stevens, Gillian Conquest, Jerome Lewis, and Muki Haklay. 2017. Supporting Collaboration with Non-Literate Forest Communities in the Congo-Basin. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17). ACM. https://doi.org/10.1145/2998181.2998242 Waldman (2021) Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). 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(2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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(2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. 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(2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ari Ezra Waldman. 2021. Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power. Cambridge University Press. https://doi.org/10.1017/9781108591386 Walter and Suina (2019) Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). 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(2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. 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(2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! 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(2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. 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(2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. 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Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). 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(2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. 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Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. 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(2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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(2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. 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Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. 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ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Maggie Walter and Michele Suina. 2019. Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology (2019). Wang et al. (2022b) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022b. Whose AI Dream? In Search of the Aspiration in Data Annotation.. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502121 Wang et al. (2019a) Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. (Nov. 2019). https://doi.org/10.1145/3359313 Wang et al. (2019b) Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. 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(2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. 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(2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. 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Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). 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Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. 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ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. 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(2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019b. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM. https://doi.org/10.1145/3290605.3300831 Wang et al. (2022c) Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. 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Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. 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Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. 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Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022c. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3502057 Wang et al. (2021) Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. 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(2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. 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(2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. 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Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. 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ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. CASS: Towards Building a Social-Support Chatbot for Online Health Community. Proc. ACM Hum.-Comput. Interact. (April 2021). https://doi.org/10.1145/3449083 Wang et al. (2022a) Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. 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ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. 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ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Ida Camacho, Shan Jing, and Ashok K. Goel. 2022a. Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proc. ACM Hum.-Comput. Interact. (April 2022). https://doi.org/10.1145/3512977 Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). 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Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. 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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. 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(April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581278 Washington and Kuo (2020) Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). 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(2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. 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ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Anne L. Washington and Rachel Kuo. 2020. Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). ACM. https://doi.org/10.1145/3351095.3372844 Watkins (2023) Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Elizabeth Anne Watkins. 2023. Face Work: A Human-Centered Investigation into Facial Verification in Gig Work. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579485 Weick (1995) Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Karl E Weick. 1995. Sensemaking in organizations. Sage. https://us.sagepub.com/en-us/nam/sensemaking-in-organizations/book4988 Weidinger et al. (2022) Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022. Taxonomy of Risks Posed by Language Models. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533088 Whittlestone et al. (2019) Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! 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Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. 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Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. 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ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! 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Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. 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Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). ACM. https://doi.org/10.1145/3306618.3314289 Widder et al. (2022) David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. 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Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. 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(2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. 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(2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022. Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533779 Wilkinson et al. (2020) Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. 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(2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Dominic Wilkinson, Hazem Zohny, Andreas Kappes, Walter Sinnott-Armstrong, and Julian Savulescu. 2020. Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas. BMJ open (2020). https://doi.org/10.1136/bmjopen-2020-045593 Willen et al. (2022) Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. 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Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. 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(2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. 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Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sarah S. Willen, Abigail Fisher Williamson, Colleen C. Walsh, Mikayla Hyman, and William Tootle. 2022. Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest. SSM - Mental Health (2022). https://doi.org/10.1016/j.ssmmh.2021.100057 Winecoff and Watkins (2022) Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Amy A. Winecoff and Elizabeth Anne Watkins. 2022. Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534138 Wong et al. (2023a) Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. 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(2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. 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In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Andrew Chong, and R. Cooper Aspegren. 2023a. Privacy Legislation as Business Risks: How GDPR and CCPA Are Represented in Technology Companies’ Investment Risk Disclosures. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579515 Wong et al. (2023b) Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. 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ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023b. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579621 Xu et al. (2022) Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. 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This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine (2022). https://doi.org/10.1016/j.ebiom.2022.104250 Yang et al. (2023) Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581393 Yang et al. (2022) Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. 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ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. 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Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). 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(2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. 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Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Yu Yang, Aayush Gupta, Jianwei Feng, Prateek Singhal, Vivek Yadav, Yue Wu, Pradeep Natarajan, Varsha Hedau, and Jungseock Joo. 2022. Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’22). ACM. https://doi.org/10.1145/3514094.3534153 Yfantidou et al. (2023) Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, and Fahim Kawsar. 2023. Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing. arXiv preprint arXiv:2303.15585 (2023). Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, Eoin Ó Loideáin, Azzurra Pini, Medb Corcoran, Jeremiah Hayes, Diarmuid J Cahalane, Gaurav Shivhare, Luigi Castoro, Giovanni Caruso, Changhoon Oh, James McCann, Jodi Forlizzi, and John Zimmerman. 2022. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517491 Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580900 Young (2021) James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581131 Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581318 Zytko et al. (2022) Douglas Zytko, Pamela J. Wisniewski, Shion Guha, Eric P. S. Baumer, and Min Kyung Lee. 2022. Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 James Young. 2021. Danger! This robot may be trying to manipulate you. Science Robotics (2021). https://doi.org/10.1126/scirobotics.abk3479 Young et al. (2022) Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM. https://doi.org/10.1145/3491102.3517522 Zheng et al. (2023) Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, and Xiaojuan Ma. 2023. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. Towards a Multi-Stakeholder Value-Based Assessment Framework for Algorithmic Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533118 Yuval Noah Harari (2021) Yuval Noah Harari. 2021. Lessons from a year of Covid. 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Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). ACM, Article 154. https://doi.org/10.1145/3491101.3516506 Meg Young, Michael Katell, and P.M. Krafft. 2022. Confronting Power and Corporate Capture at the FAccT Conference. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). ACM. https://doi.org/10.1145/3531146.3533194 Yuan et al. (2023) Chien Wen (Tina) Yuan, Nanyi Bi, Ya-Fang Lin, and Yuen-Hsien Tseng. 2023. Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3580945 Yurrita et al. (2022) Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, and Alessandro Bozzon. 2022. 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Financial Times. Retrieved January 2023 from https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841 Zhang et al. (2023a) Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023a. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). ACM. https://doi.org/10.1145/3544548.3581354 Zhang et al. (2023b) Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023b. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proc. ACM Hum.-Comput. Interact. (April 2023). https://doi.org/10.1145/3579601 Zhang et al. (2022) Wencan Zhang, Mariella Dimiccoli, and Brian Y Lim. 2022. Debiased-CAM to Mitigate Image Perturbations with Faithful Visual Explanations of Machine Learning. 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Authors (4)
  1. Mohammad Tahaei (10 papers)
  2. Marios Constantinides (35 papers)
  3. Daniele Quercia (77 papers)
  4. Michael Muller (70 papers)
Citations (6)
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