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"If the Machine Is As Good As Me, Then What Use Am I?" -- How the Use of ChatGPT Changes Young Professionals' Perception of Productivity and Accomplishment (2404.12549v1)

Published 19 Apr 2024 in cs.HC
"If the Machine Is As Good As Me, Then What Use Am I?" -- How the Use of ChatGPT Changes Young Professionals' Perception of Productivity and Accomplishment

Abstract: LLMs like ChatGPT have been widely adopted in work contexts. We explore the impact of ChatGPT on young professionals' perception of productivity and sense of accomplishment. We collected LLMs' main use cases in knowledge work through a preliminary study, which served as the basis for a two-week diary study with 21 young professionals reflecting on their ChatGPT use. Findings indicate that ChatGPT enhanced some participants' perceptions of productivity and accomplishment by enabling greater creative output and satisfaction from efficient tool utilization. Others experienced decreased perceived productivity and accomplishment, driven by a diminished sense of ownership, perceived lack of challenge, and mediocre results. We found that the suitability of task delegation to ChatGPT varies strongly depending on the task nature. It's especially suitable for comprehending broad subject domains, generating creative solutions, and uncovering new information. It's less suitable for research tasks due to hallucinations, which necessitate extensive validation.

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Analyzing the Impact of ChatGPT on Young Professionals' Perceptions of Productivity and Accomplishment

Introduction and Research Goals

This paper explores the complex dynamics between generative AI, specifically ChatGPT, and its adoption by young professionals in knowledge work environments. The dual focus of the research investigates how such tools influence both perceived productivity and the individual's sense of personal accomplishment.

Methodology

Initial Setup and Participant Profile

The research was structured in two phases: a preliminary paper and a subsequent diary paper. Initially, semi-structured interviews identified use cases for LLMs which then informed the diary paper's design. The participants, all enrolled in a master's program centered on entrepreneurship and innovation, provided insights into their daily interaction with ChatGPT over two weeks.

Diary Study Execution

Participants documented their use of ChatGPT, capturing experiences and evaluations of productivity and accomplishment. This method allowed the researchers to gather qualitative data in a real-world setting, thereby enhancing the reliability of the findings.

Key Findings

Impact on Productivity

The paper outlined numerous positive impacts of ChatGPT on productivity:

  • Time Efficiency and Output: ChatGPT allowed participants to complete tasks more quickly and produce more output, enhancing their perceived efficiency.
  • Outsourcing Routine Tasks: ChatGPT took on routine or less preferred tasks, freeing up participants to focus on more critical activities.
  • Information Gathering: ChatGPT facilitated quicker preliminary research, providing a foundational understanding which participants could then expand upon.

However, productivity barriers were also evident. These included occasional unreliability of generated content, necessitating cross-verification, and issues with the quality of text outputs like grammar and pertinence, therefore requiring significant post-processing.

Sense of Personal Accomplishment

ChatGPT's role also brought several nuances to light regarding personal accomplishment:

  • Ownership and Contribution: While ChatGPT was helpful, a clear demarcation of personal intellectual input remained crucial. Participants valued their original contributions over the AI-generated content.
  • Strategic Use: Success in effectively utilizing ChatGPT to fulfill specific needs boosted feelings of accomplishment.
  • Task Completion: Successfully completing tasks with the aid of ChatGPT was noted as a positive factor.

Nevertheless, several factors dampened the sense of accomplishment:

  • Diminished Personal Input: Heavy reliance on ChatGPT sometimes led participants to feel detached from the output, reducing their sense of accomplishment.
  • Complexity of Tasks: Simple tasks that required minimal human intervention despite using AI did not significantly enhance feelings of accomplishment.

Discussion and Implications

The findings suggest a nuanced view of how generative AI tools like ChatGPT are reshaping the landscape of knowledge work. On one hand, there's a measurable boost in productivity and efficiency. On the other, the balance of AI involvement and human input critical for fostering a sense of personal accomplishment is delicate.

It is essential for future tool development and workplace integration strategies to consider these dynamics. Providing young professionals with the skills to effectively collaborate with AI will be crucial in maximizing both productivity gains and individual satisfaction in their professional roles.

Future Research Directions

This paper opens up several avenues for future research:

  • Broader Demographic Analysis: Examining a wider range of professions and experience levels could provide more generalized insights.
  • Long-term Impact Studies: Longitudinal studies could track changes in perceptions and outcomes as users and AI tools evolve.
  • AI Tool Enhancement: Exploring the development of AI tools that can adapt to individual working styles and offer more personalized assistance.

By continuing to investigate these crucial interplays between AI tools and human workers, the field can progress towards more effective and satisfying integrations of technology in the workplace.

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References (89)
  1. Daron Acemoglu and Pascual Restrepo. 2018. The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. American Economic Review 108, 6 (June 2018), 1488–1542. https://doi.org/10.1257/aer.20160696
  2. Daron Acemoglu and Pascual Restrepo. 2019. Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives 33, 2 (May 2019), 3–30. https://doi.org/10.1257/jep.33.2.3
  3. Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives 33 (May 2019), 31–50. https://doi.org/10.1257/jep.33.2.31
  4. Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications 10, 1 (June 2023), 311. https://doi.org/10.1057/s41599-023-01787-8
  5. Albert Bandura. 1986. The Explanatory and Predictive Scope of Self-Efficacy Theory. Journal of Social and Clinical Psychology 4, 3 (Sept. 1986), 359–373. https://doi.org/10.1521/jscp.1986.4.3.359
  6. Albert Bandura. 1997. Self-efficacy: The exercise of control. Freeman, New York, NY, USA.
  7. Albert Bandura. 1999. Social Cognitive Theory: An Agentic Perspective. Asian Journal of Social Psychology 2, 1 (April 1999), 21–41. https://doi.org/10.1111/1467-839X.00024
  8. Hyejin Bang and Thomas G. Reio. 2016. Personal Accomplishment, Mentoring, and Creative Self-Efficacy as Predictors of Creative Work Involvement: The Moderating Role of Positive and Negative Affect. The Journal of Psychology 151, 2 (Nov. 2016), 148–170. https://doi.org/10.1080/00223980.2016.1248808
  9. Capturing Value from Artificial Intelligence. Academy of Management Discoveries 9, 4 (April 2023), 424–428. https://doi.org/10.5465/amd.2023.0106 Publisher: Academy of Management.
  10. Automatic Reaction - What Happens to Workers at Firms that Automate? Technical Report. Boston University School of Law. https://www.ssrn.com/abstract=3328877
  11. From Tool to Companion: Storywriters Want AI Writers to Respect Their Personal Values and Writing Strategies. In Proceedings of the 2022 ACM Designing Interactive Systems Conference (DIS ’22). Association for Computing Machinery, New York, NY, USA, 1209–1227. https://doi.org/10.1145/3532106.3533506
  12. Diary Methods: Capturing Life as it is Lived. Annual Review of Psychology 54, 1 (Feb. 2003), 579–616. https://doi.org/10.1146/annurev.psych.54.101601.145030
  13. Automation after the Assembly Line: Computerized Machine Tools, Employment and Productivity in the United States. https://doi.org/10.2139/ssrn.4203066
  14. Human and machine: The impact of machine input on decision-making under cognitive limitations. Technical Report. ESMT Berlin Working Paper.
  15. Robert Brandl. 2023. ChatGPT Statistics and User Numbers 2023 - OpenAI Chatbot. https://www.tooltester.com/en/blog/chatgpt-statistics/
  16. Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (Jan. 2006), 77–101. https://doi.org/10.1191/1478088706qp063oa
  17. Abraham Carmeli and John Schaubroeck. 2007. The influence of leaders’ and other referents’ normative expectations on individual involvement in creative work. The Leadership Quarterly 18, 1 (Feb. 2007), 35–48. https://doi.org/10.1016/j.leaqua.2006.11.001
  18. Scott Carter and Jennifer Mankoff. 2005. When participants do the capturing: the role of media in diary studies. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’05). Association for Computing Machinery, New York, NY, USA, 899–908. https://doi.org/10.1145/1054972.1055098
  19. Andrew R. Chow. 2023. How ChatGPT Managed to Grow Faster Than TikTok or Instagram. https://time.com/6253615/chatgpt-fastest-growing/
  20. TaleBrush: Sketching Stories with Generative Pretrained Language Models. In CHI Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–19. https://doi.org/10.1145/3491102.3501819
  21. Rikke Friis Dam and Teo Yu Siang. 2022. Affinity Diagrams: How to Cluster Your Ideas and Reveal Insights. https://www.interaction-design.org/literature/article/affinity-diagrams-learn-how-to-cluster-and-bundle-ideas-and-facts
  22. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Technical Report. Harvard Business School Technology & Operations Management Working Paper. https://www.ssrn.com/abstract=4573321
  23. The Idea Machine: LLM-based Expansion, Rewriting, Combination, and Suggestion of Ideas. In Proceedings of the 14th Conference on Creativity and Cognition (C&C ’22). Association for Computing Machinery, New York, NY, USA, 623–627. https://doi.org/10.1145/3527927.3535197
  24. The AI Ghostwriter Effect: Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors. http://arxiv.org/abs/2303.03283 arXiv:2303.03283 [cs].
  25. Peter F. Drucker. 1999. Knowledge-Worker Productivity: The Biggest Challenge. California Management Review 41, 2 (Jan. 1999), 79–94. https://doi.org/10.2307/41165987 Publisher: SAGE Publications Inc.
  26. Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management 71 (Aug. 2023), 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  27. Andrew J. Elliot and Holly A. McGregor. 1999. Test anxiety and the hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology 76, 4 (1999), 628–644. https://doi.org/10.1037/0022-3514.76.4.628 Place: US Publisher: American Psychological Association.
  28. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. https://doi.org/10.48550/arXiv.2303.10130 arXiv:2303.10130 [cs, econ, q-fin].
  29. Karen L. Ferguson and Thomas G. Reio. 2010. Human resource management systems and firm performance. Journal of Management Development 29, 5 (Jan. 2010), 471–494. https://doi.org/10.1108/02621711011039231 Publisher: Emerald Group Publishing Limited.
  30. Modeling and Guiding the Creation of Ethical Human-AI Teams. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’21). Association for Computing Machinery, New York, NY, USA, 469–479. https://doi.org/10.1145/3461702.3462573
  31. Carl Benedikt Frey and Michael A. Osborne. 2017. The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change 114 (Jan. 2017), 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
  32. Jennifer George and Jing Zhou. 2001. When Openness to Experience and Conscientiousness are Related to Creative Behavior: An Interactional Approach. The Journal of applied psychology 86 (July 2001), 513–24. https://doi.org/10.1037//0021-9010.86.3.513
  33. GenAI Will Change How We Design Jobs. Here’s How. https://hbr.org/2023/12/genai-will-change-how-we-design-jobs-heres-how
  34. ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks. Proceedings of the National Academy of Sciences 120, 30 (July 2023), e2305016120. https://doi.org/10.1073/pnas.2305016120 arXiv:2303.15056 [cs].
  35. Methods of data collection in qualitative research: interviews and focus groups. British Dental Journal 204, 6 (March 2008), 291–295. https://doi.org/10.1038/bdj.2008.192
  36. Is Your Time Well Spent? Reflecting on Knowledge Work More Holistically. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–9. https://doi.org/10.1145/3313831.3376586
  37. AI-Mediated Communication: Definition, Research Agenda, and Ethical Considerations. Journal of Computer-Mediated Communication 25, 1 (March 2020), 89–100. https://doi.org/10.1093/jcmc/zmz022
  38. Marc Hassenzahl. 2007. The hedonic/pragmatic model of user experience. In Towards a UX Manifesto. BCS Learning \& Development Ltd., Swindon, GBR, 10–14. https://scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2007.95
  39. Needs, affect, and interactive products - Facets of user experience. Interacting with Computers 22, 5 (Sept. 2010), 353–362. https://doi.org/10.1016/j.intcom.2010.04.002
  40. Hedonic and ergonomic quality aspects determine a software’s appeal. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (CHI ’00). Association for Computing Machinery, New York, NY, USA, 201–208. https://doi.org/10.1145/332040.332432
  41. Marc Hassenzahl and Virpi Roto. 2007. Being and doing: A perspective on user experience and its measurement. Interfaces 72 (Jan. 2007), 10–12.
  42. Yugo Hayashi and Kosuke Wakabayashi. 2017. Can AI become Reliable Source to Support Human Decision Making in a Court Scene?. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17 Companion). Association for Computing Machinery, New York, NY, USA, 195–198. https://doi.org/10.1145/3022198.3026338
  43. Monique Hennink and Bonnie N. Kaiser. 2022. Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine 292 (Jan. 2022), 114523. https://doi.org/10.1016/j.socscimed.2021.114523
  44. Discretion in Hiring. Quarterly Journal of Economics 133 (May 2018), 765–800. https://doi.org/10.1093/qje/qjx042
  45. W. David Holford. 2019. The future of human creative knowledge work within the digital economy. Futures 105 (Jan. 2019), 143–154. https://doi.org/10.1016/j.futures.2018.10.002
  46. Creative Self-Efficacy and Innovative Behavior in a Service Setting: Optimism as a Moderator. The Journal of Creative Behavior 45, 4 (Dec. 2011), 258–272. https://doi.org/10.1002/j.2162-6057.2011.tb01430.x
  47. AI, Skill, and Productivity: The Case of Taxi Drivers. Technical Report. NBR Working Paper No. w30612. https://ssrn.com/abstract=4262596
  48. Creativity as a mediator between personal accomplishment and task performance: A multigroup analysis based on gender during the COVID-19 pandemic. Current Psychology (New Brunswick, N.j.) 42, 1 (Jan. 2022), 1–13. https://doi.org/10.1007/s12144-021-02510-z
  49. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences 103 (April 2023), 102274. https://doi.org/10.1016/j.lindif.2023.102274
  50. Understanding Personal Productivity: How Knowledge Workers Define, Evaluate, and Reflect on Their Productivity. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300845
  51. Human Decision and Machine Predictions. The Quaterly Journal of Economics 133, 1 (2017), 237–293. https://doi.org/10.1093/qje/qjx032
  52. Agency in Co-Creativity: Towards a Structured Analysis of a Concept. In ICCC 2021 - 12th International Conference on Computational Creativity. Association for Computational Creativity (ACC), Mexico City (online), Mexico, 449–452. https://inria.hal.science/hal-03533245
  53. CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). Association for Computing Machinery, New York, NY, USA, 1–19. https://doi.org/10.1145/3491102.3502030
  54. User Experience Design Professionals’ Perceptions of Generative Artificial Intelligence. https://doi.org/10.48550/ARXIV.2309.15237 Publisher: arXiv Version Number: 1.
  55. R. Likert. 1932. A technique for the measurement of attitudes. Archives of Psychology 22 140 (1932), 55–55.
  56. Sai Loo. 2017. Creative Working in the Knowledge Economy. Routledge, 711 Third Avenue, New York, NY 10017.
  57. The Scope of ChatGPT in Software Engineering: A Thorough Investigation. https://doi.org/10.48550/arXiv.2305.12138 arXiv:2305.12138 [cs].
  58. Large language models generate functional protein sequences across diverse families. Nature Biotechnology 41, 8 (Aug. 2023), 1099–1106. https://doi.org/10.1038/s41587-022-01618-2 Number: 8 Publisher: Nature Publishing Group.
  59. Designing for Mutually Beneficial Decision Making in Human-Agent Teaming. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, 1 (Sept. 2022), 392–396. https://doi.org/10.1177/1071181322661358 Publisher: SAGE Publications Inc.
  60. The Maslach Burnout Inventory Manual. In Evaluating Stress: A Book of Resources. Vol. 3. The Scarecrow Press, New York, 191–218. Journal Abbreviation: Evaluating Stress: A Book of Resources.
  61. Christina Maslach and Susan E. Jackson. 1981. The measurement of experienced burnout. Journal of Organizational Behavior 2, 2 (April 1981), 99–113. https://doi.org/10.1002/job.4030020205
  62. Devadas Menon and K Shilpa. 2023. “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to Use the Open AI’s ChatGPT using the UTAUT model. Heliyon 9, 11 (Nov. 2023), e20962. https://doi.org/10.1016/j.heliyon.2023.e20962
  63. Hannah Mieczkowski and Jeffrey Hancock. 2022. Examining Agency, Expertise, and Roles of AI Systems in AI-Mediated Communication. preprint. Open Science Framework. https://doi.org/10.31219/osf.io/asnv4
  64. Temporal Adjustments in the Evaluation of Events: The “Rosy View”. Journal of Experimental Social Psychology 33, 4 (July 1997), 421–448. https://doi.org/10.1006/jesp.1997.1333
  65. Ethan R. Mollick and Lilach Mollick. 2023. Assigning AI: Seven Approaches for Students, with Prompts. Technical Report. University of Pennsylvania - Wharton School. https://www.ssrn.com/abstract=4475995
  66. Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods 16, 1 (Dec. 2017), 1609406917733847. https://doi.org/10.1177/1609406917733847 Publisher: SAGE Publications Inc.
  67. Shakked Noy and Whitney Zhang. 2023. Experimental evidence on the productivity effects of generative artificial intelligence. American Association for the Advancement of Science 381, 6654 (July 2023), 187 – 192. https://doi.org/10.1126/science.adh2586
  68. I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3173574.3174223
  69. ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies 47, 4 (2023), 1213–1225. https://doi.org/10.1111/ijcs.12928 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ijcs.12928.
  70. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. https://doi.org/10.48550/arXiv.2302.06590 arXiv:2302.06590 [cs].
  71. The State of Psychological Ownership: Integrating and Extending a Century of Research. Review of General Psychology 7, 1 (March 2003), 84–107. https://doi.org/10.1037/1089-2680.7.1.84 Publisher: SAGE Publications Inc.
  72. Rogelio Puente-Díaz. 2015. Creative Self-Efficacy: An Exploration of Its Antecedents, Consequences, and Applied Implications. The Journal of psychology 150 (Oct. 2015), 1–25. https://doi.org/10.1080/00223980.2015.1051498
  73. Yuri W. Ramírez and David A. Nembhard. 2004. Measuring knowledge worker productivity: A taxonomy. Journal of Intellectual Capital 5, 4 (Jan. 2004), 602–628. https://doi.org/10.1108/14691930410567040 Publisher: Emerald Group Publishing Limited.
  74. Transforming boundaries: how does ChatGPT change knowledge work? Journal of Business Strategy ahead-of-print, ahead-of-print (July 2023), ahead–of–print. https://doi.org/10.1108/JBS-05-2023-0094
  75. Kevin Roose. 2023. How ChatGPT Kicked Off an A.I. Arms Race. https://www.nytimes.com/2023/02/03/technology/chatgpt-openai-artificial-intelligence.html
  76. Richard M Ryan and Edward L Deci. 2000. Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. American Psychologist 55, 1 (2000), 68–78. https://doi.org/10.1037110003-066X.55.1.68
  77. Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges. https://doi.org/10.48550/arXiv.2308.05391 arXiv:2308.05391 [cs].
  78. What is satisfying about satisfying events? Testing 10 candidate psychological needs. Journal of Personality and Social Psychology 80, 2 (2001), 325–339. https://doi.org/10.1037/0022-3514.80.2.325
  79. Aneta Sokół and Irena Figurska. 2021. The Importance of Creative Knowledge Workers in Creative Organization. Energies 14, 20 (Jan. 2021), 6751. https://doi.org/10.3390/en14206751 Number: 20 Publisher: Multidisciplinary Digital Publishing Institute.
  80. Robert J. Sternberg and Todd I. Lubart. 1999. The concept of creativity: Prospects and paradigms. In Handbook of creativity. Cambridge University Press, New York, NY, US, 3–15.
  81. Daniel Susskind. 2017. A Model of Technological Unemployment. Technical Report. Oxford Handbook of AI Governance. https://www.semanticscholar.org/paper/A-Model-of-Technological-Unemployment-Susskind/ca10d575bc84eb16f0d5fda58bd1cf06c26adb7f
  82. Pamela Tierney and Steven Farmer. 2002. Creative Self-Efficacy: Its Potential Antecedents and Relationship to Creative Performance. Academy of Management Journal 45 (Dec. 2002), 1137–1148. https://doi.org/10.2307/3069429
  83. Pamela Tierney and Steven M. Farmer. 2004. The Pygmalion Process and Employee Creativity. Journal of Management 30, 3 (June 2004), 413–432. https://doi.org/10.1016/j.jm.2002.12.001
  84. ChatGPT in ophthalmology: the dawn of a new era? Eye 38 (June 2023), 4–7. https://doi.org/10.1038/s41433-023-02619-4
  85. Kerrie Unsworth. 2005. Creative Requirement: A Neglected Construct in the Study of Employee Creativity? Group & Organization Management 30 (Oct. 2005), 541–560. https://doi.org/10.1177/1059601104267607
  86. Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22). Association for Computing Machinery, New York, NY, USA, 1–7. https://doi.org/10.1145/3491101.3519665
  87. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly 36, 1 (2012), 157. https://doi.org/10.2307/41410412
  88. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (Nov. 2019), 211:1–211:24. https://doi.org/10.1145/3359313
  89. ”An Ideal Human”: Expectations of AI Teammates in Human-AI Teaming. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (Jan. 2021), 246:1–246:25. https://doi.org/10.1145/3432945
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Authors (4)
  1. Charlotte Kobiella (1 paper)
  2. Yarhy Said Flores López (1 paper)
  3. Fiona Draxler (4 papers)
  4. Albrecht Schmidt (31 papers)
Citations (3)
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