Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
131 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit (2402.13437v1)

Published 21 Feb 2024 in cs.HC

Abstract: Advances in AI have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (150)
  1. Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–18.
  2. Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems. 1–13.
  3. Apple. 2019. Human Interface Guidelines: Machine Learning. https://developer.apple.com/design/human-interface-guidelines/technologies/machine-learning/introduction/
  4. Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–20.
  5. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–12.
  6. Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance. Data & Policy 5 (2023), e12.
  7. Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–14.
  8. Supporting communication about values between people with multiple chronic conditions and their providers. In proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–14.
  9. Power to the People? Opportunities and Challenges for Participatory AI. Equity and Access in Algorithms, Mechanisms, and Optimization (2022), 1–8.
  10. Sara Bly and Elizabeth F Churchill. 1999. Design through matchmaking: technology in search of users. interactions 6, 2 (1999), 23–31.
  11. Connected baby bottle: A design case study towards a framework for data-enabled design. In Proceedings of the 2016 ACM conference on designing interactive systems. 301–311.
  12. Claus Bossen and Kathleen H Pine. 2023. Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists. ACM Transactions on Computer-Human Interaction 30, 2 (2023), 1–29.
  13. Overcoming failures of imagination in AI infused system development and deployment. arXiv preprint arXiv:2011.13416 (2020).
  14. Tone Bratteteig and Guri Verne. 2018. Does AI make PD obsolete? exploring challenges from artificial intelligence to participatory design. In Proceedings of the 15th Participatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial-Volume 2. 1–5.
  15. To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–21.
  16. Healthcare AI Treatment Decision Support: Design Principles to Enhance Clinician Adoption and Trust. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–19.
  17. Bill Buxton. 2010. Sketching user experiences: getting the design right and the right design. Morgan kaufmann.
  18. Beepless: Using Peripheral Interaction in an Intensive Care Setting. In Proceedings of the 2019 on Designing Interactive Systems Conference. 607–620.
  19. ” Hello AI”: uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making. Proceedings of the ACM on Human-computer Interaction 3, CSCW (2019), 1–24.
  20. Onboarding Materials as Cross-functional Boundary Objects for Developing AI Assistants. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. 1–7.
  21. Jared J Cash. 2009. Alert fatigue. American Journal of Health-System Pharmacy 66, 23 (2009), 2098–2101.
  22. Soliciting stakeholders’ fairness notions in child maltreatment predictive systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–17.
  23. Vibrotactile alarm display for critical care. In Proceedings of the 7th ACM International Symposium on Pervasive Displays. 1–7.
  24. A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research. In CHI Conference on Human Factors in Computing Systems. 1–18.
  25. Claire Craig. 2020. Context is everything. , 139–141 pages.
  26. Helen Cunningham and Stephen Reay. 2019. Co-creating design for health in a city hospital: perceptions of value, opportunity and limitations from ‘Designing Together’symposium. Design for Health 3, 1 (2019), 119–134.
  27. An Uncommon Task: Participatory Design in Legal AI. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2022), 1–23.
  28. Stakeholder Participation in AI: Beyond” Add Diverse Stakeholders and Stir”. arXiv preprint arXiv:2111.01122 (2021).
  29. The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. 1–23.
  30. Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. arXiv preprint arXiv:2205.06922 (2022).
  31. Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 705–716.
  32. Health products; designed with, not for, end users. (2011).
  33. A systematic review of teamwork in the intensive care unit: what do we know about teamwork, team tasks, and improvement strategies? Journal of critical care 29, 6 (2014), 908–914.
  34. Graham Dove and Anne-Laure Fayard. 2020. Monsters, metaphors, and machine learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–17.
  35. UX design innovation: Challenges for working with machine learning as a design material. In Proceedings of the 2017 chi conference on human factors in computing systems. 278–288.
  36. Graham Dove and Sara Jones. 2014. Using data to stimulate creative thinking in the design of new products and services. In Proceedings of the 2014 conference on Designing interactive systems. 443–452.
  37. Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. Journal of Biomedical Informatics 132 (2022), 104107.
  38. Beyond the hype: why do data-driven projects fail?. In Proceedings of the 54th Hawaii International Conference on System Sciences. 5081.
  39. From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. 38–48.
  40. How Do UX Practitioners Communicate AI as a Design Material? Artifacts, Conceptions, and Propositions. In Proceedings of the 2023 ACM Designing Interactive Systems Conference. 2263–2280.
  41. Jodi Forlizzi. 2018. Moving beyond user-centered design. Interactions 25, 5 (2018), 22–23.
  42. Artificial intelligence and multidisciplinary team meetings; a communication challenge for radiologists’ sense of agency and position as spider in a web? European Journal of Radiology 155 (2022), 110231.
  43. Datasheets for datasets. Commun. ACM 64, 12 (2021), 86–92.
  44. Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-Technical Perspective. ACM Transactions on Computer-Human Interaction (2023).
  45. Association of Unit Census with Delays in Antimicrobial Initiation among Ward Patients with Hospital-acquired Sepsis. Annals of the American Thoracic Society ja (2022).
  46. Fabien Girardin and Neal Lathia. 2017. When user experience designers partner with data scientists. In 2017 AAAI Spring Symposium Series.
  47. Google. 2022. Machine Learning Guides: Text Classification. https://developers.google.com/machine-learning/guides/text-classification
  48. Fast, structured clinical documentation via contextual autocomplete. In Machine Learning for Healthcare Conference. PMLR, 842–870.
  49. Nielsen Norman Group. 2018. Using Prioritization Matrices to Inform UX Decisions. Retrieved September 14, 2022 from https://www.nngroup.com/articles/prioritization-matrices/
  50. Aaron Halfaker and R Stuart Geiger. 2020. Ores: Lowering barriers with participatory machine learning in wikipedia. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1–37.
  51. Douglas D Heckathorn. 2011. Comment: Snowball versus respondent-driven sampling. Sociological methodology 41, 1 (2011), 355–366.
  52. Sean M Hickey and Al O Giwa. 2020. Mechanical Ventilation. In StatPearls. StatPearls Publishing, Treasure Island (FL).
  53. Designing contestability: Interaction design, machine learning, and mental health. In Proceedings of the 2017 Conference on Designing Interactive Systems. 95–99.
  54. Improving fairness in machine learning systems: What do industry practitioners need?. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–16.
  55. Shifting concepts of value: Designing algorithmic decision-support systems for public services. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society. 1–12.
  56. Designing Data: Proactive Data Collection and Iteration for Machine Learning. arXiv preprint arXiv:2301.10319 (2023).
  57. Design research in healthcare: a systematic literature review of key design journals. Journal of Engineering Design 33, 8-9 (2022), 522–544.
  58. IDEO. 2009. The Human-Centered Design Toolkit. https://www.designkit.org/
  59. Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–14.
  60. Anniek Jansen and Sara Colombo. 2023. Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions. In Proceedings of the Seventeenth International Conference on Tangible, Embedded, and Embodied Interaction. 1–18.
  61. Inclusion of clinicians in the development and evaluation of clinical artificial intelligence tools: a systematic literature review. Frontiers in Psychology 13 (2022).
  62. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data 6, 1 (2019), 317.
  63. Why so many data science projects fail to deliver. MIT Sloan Management Review (2021).
  64. Hospital volume and the outcomes of mechanical ventilation. New England Journal of Medicine 355, 1 (2006), 41–50.
  65. ” You Have to Piece the Puzzle Together” Implications for Designing Decision Support in Intensive Care. In Proceedings of the 2020 ACM Designing Interactive Systems Conference. 1509–1522.
  66. Holtzblatt Karen and Jones Sandra. 2017. Contextual inquiry: A participatory technique for system design. In Participatory design. CRC Press, 177–210.
  67. Improving human-AI partnerships in child welfare: understanding worker practices, challenges, and desires for algorithmic decision support. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–18.
  68. “Why Do I Care What’s Similar?” Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts. In Designing Interactive Systems Conference. 454–470.
  69. Identifying the intersections: User experience+ research scientist collaboration in a generative machine learning interface. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1–8.
  70. A Case Study of Data-Enabled Design for Cardiac Telemonitoring. In Proceedings of the European Conference on Cognitive Ergonomics 2023. 1–7.
  71. A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds. Journal of Biomedical Informatics 146 (2023), 104483.
  72. Maaike Kleinsmann and Martijn Ten Bhömer. 2020. The (new) roles of prototypes during the co-development of digital product service systems. International Journal of Design 14, 1 (2020), 65–79.
  73. Will you accept an imperfect ai? exploring designs for adjusting end-user expectations of ai systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–14.
  74. Sean Kross and Philip Guo. 2021. Orienting, framing, bridging, magic, and counseling: How data scientists navigate the outer loop of client collaborations in industry and academia. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–28.
  75. Understanding Frontline Workers’ and Unhoused Individuals’ Perspectives on AI Used in Homeless Services. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–17.
  76. Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–24.
  77. Questioning the AI: informing design practices for explainable AI user experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–15.
  78. Designerly understanding: Information needs for model transparency to support design ideation for AI-powered user experience. In Proceedings of the 2023 CHI conference on human factors in computing systems. 1–21.
  79. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PloS one 14, 7 (2019), e0218942.
  80. Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI. arXiv preprint arXiv:2308.07213 (2023).
  81. How data scientistswork together with domain experts in scientific collaborations: To find the right answer or to ask the right question? Proceedings of the ACM on Human-Computer Interaction 3, GROUP (2019), 1–23.
  82. Universal methods of design: 100 ways to research complex problems. Develop Innovative Ideas, and Design Effective Solutions (2012), 12–13.
  83. Sedation, delirium and mechanical ventilation: the ‘ABCDE’approach. Current opinion in critical care 17, 1 (2011), 43–49.
  84. Imagining artificial intelligence applications with people with visual disabilities using tactile ideation. In Proceedings of the 19th international acm sigaccess conference on computers and accessibility. 81–90.
  85. Camille Moussette and Richard Banks. 2010. Designing through making: exploring the simple haptic design space. In Proceedings of the fifth international conference on Tangible, embedded, and embodied interaction. 279–282.
  86. Grasping AI: experiential exercises for designers. AI & SOCIETY (2023), 1–21.
  87. Metaphors for designers working with AI. (2022).
  88. Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process. Organization 1, 2 (2022), 3.
  89. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Critical care medicine 46, 4 (2018), 547.
  90. Breaking up data-enabled design: expanding and scaling up for the clinical context. AI EDAM 36 (2022).
  91. Michael Oppermann and Tamara Munzner. 2020. Data-first visualization design studies. In 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV). IEEE, 74–80.
  92. Realizing AI in healthcare: challenges appearing in the wild. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. 1–5.
  93. How to support designers in getting hold of the immaterial material of software. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2513–2522.
  94. Google PAIR. 2019. People + AI Guidebook. pair.withgoogle.com/guidebook
  95. Samir Passi and Solon Barocas. 2019. Problem formulation and fairness. In Proceedings of the conference on fairness, accountability, and transparency. 39–48.
  96. How ai developers overcome communication challenges in a multidisciplinary team: A case study. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–25.
  97. Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI. arXiv preprint arXiv:2204.01075 (2022).
  98. The fallacy of AI functionality. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 959–972.
  99. Initiate. collaborate: a design for health collaboration toolkit. Design for Health 5, 3 (2021), 294–312.
  100. Coordinating heterogeneous work: Information and representation in medical care. In ECSCW 2001. Springer, 239–258.
  101. Temporality in medical work: Time also matters. Computer Supported Cooperative Work (CSCW) 15, 1 (2006), 29–53.
  102. Johan Redström. 2005. On technology as material in design. Design Philosophy Papers 3, 2 (2005), 39–54.
  103. Eric Reis. 2011. The lean startup. New York: Crown Business 27 (2011), 2016–2020.
  104. Samantha Robertson and Niloufar Salehi. 2020. What If I Don’t Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design. arXiv preprint arXiv:2007.06718 (2020).
  105. Healthsheet: development of a transparency artifact for health datasets. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 1943–1961.
  106. Nithya Sambasivan and Rajesh Veeraraghavan. 2022. The Deskilling of Domain Expertise in AI Development. In CHI Conference on Human Factors in Computing Systems. 1–14.
  107. Bridging the implementation gap of machine learning in healthcare. BMJ Innovations 6, 2 (2020).
  108. Ben Shneiderman. 2020. Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction 36, 6 (2020), 495–504.
  109. Ignore, trust, or negotiate: understanding clinician acceptance of AI-based treatment recommendations in health care. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–18.
  110. Amazon us patent anticipatory shipping. Amazon Technologies Inc 12 (2014).
  111. Designing guidelines for mobile health technology: Managing notification interruptions in the ICU. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 4502–4508.
  112. Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 1162–1177.
  113. Toward harnessing user feedback for machine learning. In Proceedings of the 12th international conference on Intelligent user interfaces. 82–91.
  114. Solving Separation-of-Concerns Problems in Collaborative Design of Human-AI Systems through Leaky Abstractions. In CHI Conference on Human Factors in Computing Systems. 1–21.
  115. Inspirational bits: towards a shared understanding of the digital material. In Proceedings of the SIGCHI conference on human factors in computing systems. 1561–1570.
  116. Clinical intervention prediction and understanding using deep networks. arXiv preprint arXiv:1705.08498 (2017).
  117. Human-Centered Responsible Artificial Intelligence: Current & Future Trends. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. 1–4.
  118. Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education. arXiv preprint arXiv:2311.05792 (2023).
  119. Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction (TOCHI) 27, 5 (2020), 1–53.
  120. Designing human-centered AI for mental health: Developing clinically relevant applications for online CBT treatment. ACM Transactions on Computer-Human Interaction 30, 2 (2023), 1–50.
  121. Foundation Models in Healthcare: Opportunities, Risks & Strategies Forward. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. 1–4.
  122. “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. 1–14.
  123. 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. 1–17.
  124. Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 chi conference on human factors in computing systems. 1–14.
  125. 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. 1–16.
  126. Joyce Weiner. 2020. Why AI/data science projects fail: how to avoid project pitfalls. Synthesis Lectures on Computation and Analytics 1, 1 (2020), i–77.
  127. Jonathan West. 2020. Design in healthcare: The challenge of translation. Design for Health 4, 2 (2020), 252–269.
  128. Mikael Wiberg. 2014. Methodology for materiality: interaction design research through a material lens. Personal and ubiquitous computing 18, 3 (2014), 625–636.
  129. AI Consent Futures: A Case Study on Voice Data Collection with Clinicians. Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (2023), 1–30.
  130. Physician-driven management of patient progress notes in an intensive care unit. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1879–1888.
  131. Mapping machine learning advances from hci research to reveal starting places for design innovation. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–11.
  132. Sketching nlp: A case study of exploring the right things to design with language intelligence. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–12.
  133. Harnessing biomedical literature to calibrate clinicians’ trust in AI decision support systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–14.
  134. Investigating how experienced UX designers effectively work with machine learning. In Proceedings of the 2018 designing interactive systems conference. 585–596.
  135. Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 chi conference on human factors in computing systems. 1–13.
  136. Unremarkable ai: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–11.
  137. Investigating the heart pump implant decision process: opportunities for decision support tools to help. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 4477–4488.
  138. How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In CHI Conference on Human Factors in Computing Systems. 1–13.
  139. Creating design resources to scaffold the ideation of AI concepts. In Proceedings of the 2023 ACM Designing Interactive Systems Conference. 2326–2346.
  140. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People+ AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–13.
  141. Technical Feasibility, Financial Viability, and Clinician Acceptance: On the Many Challenges to AI in Clinical Practice.. In HUMAN@ AAAI Fall Symposium.
  142. Keeping designers in the loop: Communicating inherent algorithmic trade-offs across multiple objectives. In Proceedings of the 2020 ACM designing interactive systems conference. 1245–1257.
  143. Artificial intelligence in healthcare. Nature biomedical engineering 2, 10 (2018), 719–731.
  144. Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. 351–362.
  145. Clinician-facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI. ACM Transactions on Computer-Human Interaction 30, 2 (2023), 1–39.
  146. Sabah Zdanowska and Alex S Taylor. 2022. A study of UX practitioners roles in designing real-world, enterprise ML systems. In CHI Conference on Human Factors in Computing Systems. 1–15.
  147. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1–32.
  148. Research through design as a method for interaction design research in HCI. In Proceedings of the SIGCHI conference on Human factors in computing systems. 493–502.
  149. UX designers pushing AI in the enterprise: a case for adaptive UIs. Interactions 28, 1 (2020), 72–77.
  150. Recentering Reframing as an RtD Contribution: The Case of Pivoting from Accessible Web Tables to a Conversational Internet. In CHI Conference on Human Factors in Computing Systems. 1–14.
Citations (4)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets