skip to main content
10.1145/3544548.3580794acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

AI Knowledge: Improving AI Delegation through Human Enablement

Published:19 April 2023Publication History

ABSTRACT

When collaborating with artificial intelligence (AI), humans can often delegate tasks to leverage complementary AI competencies. However, humans often delegate inefficiently. Enabling humans with knowledge about AI can potentially improve inefficient AI delegation. We conducted a between-subjects experiment (two groups, n = 111) to examine how enabling humans with AI knowledge can improve AI delegation in human-AI collaboration. We find that AI knowledge-enabled humans align their delegation decisions more closely with their assessment of how suitable a task is for humans or AI (i.e., task appraisal). We show that delegation decisions closely aligned with task appraisal increase task performance. However, we also find that AI knowledge lowers future intentions to use AI, suggesting that AI knowledge is not strictly positive for human-AI collaboration. Our study contributes to HCI design guidelines with a new perspective on AI features, educating humans regarding general AI functioning and their own (human) performance and biases.

Footnotes

  1. 1 To reliably detect significant results, we aimed to exceed the minimum sample size of 51 in each experimental group, which was estimated through a power analysis leveraging G*Power 3.1 [33]. Therefore, we used the following parameter: a moderate effect size (f = 0.50), an α-level of 0.05, and a desired power level of 0.80 [26].

    Footnote
Skip Supplemental Material Section

Supplemental Material

3544548.3580794-talk-video.mp4

mp4

235.6 MB

References

  1. Martin Adam, Konstantin Roethke and Alexander Benlian. 2022. Human Versus Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages. Information Systems Research, Forthcoming (2022). https://doi.org/10.1287/isre.2022.1171Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Pär J. Ågerfalk. 2020. Artificial intelligence as digital agency. European Journal of Information Systems, 29, 1 (2020), 1-8. https://doi.org/10.1080/0960085x.2020.1721947Google ScholarGoogle ScholarCross RefCross Ref
  3. Amazon. 2017. Tutorial: How to label thousands of images using the crowd. Retrieved August 10, 2022 from https://blog.mturk.com/tutorial-how-to-label-thousands-of-images-using-the-crowd-bea164ccbefcGoogle ScholarGoogle Scholar
  4. Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil and Eric Horvitz. 2019. Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300233Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Eduard Anton, Alina Behne and Frank Teuteberg. 2020. The humans behind artificial intelligence - An operationalisation of AI competencies. In Proceedings of the European Conference on Information Systems. Virtual. https://aisel.aisnet.org/ecis2020_rp/141Google ScholarGoogle Scholar
  6. Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila and Francisco Herrera. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58 (2020), 82-115. https://doi.org/10.1016/j.inffus.2019.12.012Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Sangeeta Bharadwaj Badal and Bryant Ott. 2015. Delegating: A Huge Management Challenge for Entrepreneurs. Retrieved August 10, 2022 from https://news.gallup.com/businessjournal/182414/delegating-huge-management-challenge-entrepreneurs.aspxGoogle ScholarGoogle Scholar
  8. Aaron Baird and Likoebe M. Maruping. 2021. The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45, 1 (2021), 315-341. https://doi.org/10.25300/misq/2021/15882Google ScholarGoogle ScholarCross RefCross Ref
  9. Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S. Weld, Walter S. Lasecki and Eric Horvitz. 2019. Update in Human-AI Team. Understanding and Addressing the performance compatibility tradeoff. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). Honolulu, USA.Google ScholarGoogle Scholar
  10. Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro and Daniel Weld. 2021. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411764.3445717Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Reuben M. Baron and David A. Kenny. 1986. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol, 51, 6 (1986), 1173-1182. https://doi.org/10.1037//0022-3514.51.6.1173Google ScholarGoogle ScholarCross RefCross Ref
  12. Geneviève Bassellier, Izak Benbasat and Blaize Horner Reich. 2003. The Influence of Business Managers' IT Competence on Championing IT. Information Systems Research, 14, 4 (2003), 317-336. https://doi.org/10.1287/isre.14.4.317.24899Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Geneviève Bassellier, Blaize Horner Reich and Izak Benbasat. 2015. Information Technology Competence of Business Managers: A Definition and Research Model. Journal of Management Information Systems, 17, 4 (2015), 159-182. https://doi.org/10.1080/07421222.2001.11045660Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Kevin Bauer, Moritz von Zahn and Oliver Hinz. Forthcoming. Expl(AI)ned: The Impact of Explainable Articial Intelligence on Users' Information Processing. Information Systems Research (Forthcoming). https://doi.org/10.1287/xxxx.0000.0000Google ScholarGoogle ScholarCross RefCross Ref
  15. Anne Beaudry and Alain Pinsonneault. 2005. Understanding User Responses to Information Technology: A Coping Model of User Adaptation. MIS Quarterly, 29, 3 (2005). https://doi.org/10.2307/25148693Google ScholarGoogle ScholarCross RefCross Ref
  16. Hind Benbya, Stella Pachidi and Sirkka L. Jarvenpaa. 2021. Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research. Journal of the Association for Information Systems, 22, 2 (2021), 281-303. https://doi.org/10.17705/1jais.00662Google ScholarGoogle ScholarCross RefCross Ref
  17. Nicholas Berente, Bin Gu, Jan Recker and Radhika Santhanam. 2021. Managing Artificial Intelligence. MIS Quarterly, 45, 3 (2021), 1433-1450. https://doi.org/10.25300/MISQ/2021/16274Google ScholarGoogle ScholarCross RefCross Ref
  18. Anol Bhattacherjee. 2001. Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 3 (2001), 351-370. https://doi.org/10.2307/3250921Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Anol Bhattacherjee, Christopher J. Davis, Amy J. Connolly, Neset Hikmet, Frantz Rowe and Régis Meissonier. 2017. User response to mandatory IT use: a coping theory perspective. European Journal of Information Systems, 27, 4 (2017), 395-414. https://doi.org/10.1057/s41303-017-0047-0Google ScholarGoogle ScholarCross RefCross Ref
  20. Carrie J. Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S. Corrado, Martin C. Stumpe and Michael Terry. 2019. Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300234Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Wanling Cai, Yucheng Jin and Li Chen. 2022. Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3517471Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Longbing Cao. 2020. AI in Finance: A Review. SSRN Electronic Journal (2020). https://doi.org/10.2139/ssrn.3647625Google ScholarGoogle ScholarCross RefCross Ref
  23. Noel Carroll. 2021. Augmented Intelligence: An Actor-Network Theory Perspective. In Proceedings of the European Conference on Information Systems. Marrakesh, Morocco. https://aisel.aisnet.org/ecis2021_rp/37Google ScholarGoogle Scholar
  24. Dilek Cetindamar, Kirsty Kitto, Mengjia Wu, Yi Zhang, Babak Abedin and Simon Knight. 2022. Explicating AI Literacy of Employees at Digital Workplaces. IEEE Transactions on Engineering Management (2022), 1-14. https://doi.org/10.1109/tem.2021.3138503Google ScholarGoogle ScholarCross RefCross Ref
  25. Hyesun Choung, Prabu David and Arun Ross. 2022. Trust in AI and Its Role in the Acceptance of AI Technologies. International Journal of Human–Computer Interaction (2022), 1-13. https://doi.org/10.1080/10447318.2022.2050543Google ScholarGoogle ScholarCross RefCross Ref
  26. Clifford C. Clogg, Eva Petkova and Adamantios Haritou. 1995. Statistical Methods for Comparing Regression Coefficients Between Models. American Journal of Sociology, 100, 5 (1995), 1261-1293. https://doi.org/10.1086/230638Google ScholarGoogle ScholarCross RefCross Ref
  27. Jacob Cohen, Patricia Cohen, Stephan G. West and Leona S. Aiken. 2003. Applied multiple regression/correlation analysis for the behavioral sciences. Erlbaum. Hillsdale, NJGoogle ScholarGoogle Scholar
  28. W. Alec Cram, Jeffrey G. Proudfoot and John D'Arcy. 2020. When enough is enough: Investigating the antecedents and consequences of information security fatigue. Information Systems Journal, 31, 4 (2020), 521-549. https://doi.org/10.1111/isj.12319Google ScholarGoogle ScholarCross RefCross Ref
  29. Dominik Dellermann, Philipp Ebel, Matthias Söllner and Jan Marco Leimeister. 2019. Hybrid Intelligence. Business & Information Systems Engineering, 61, 5 (2019), 637-643. https://doi.org/10.1007/s12599-019-00595-2Google ScholarGoogle ScholarCross RefCross Ref
  30. Xianghua Ding, Yanqi Jiang, Xiankang Qin, Yunan Chen, Wenqiang Zhang and Lizhe Qi. 2019. Reading Face, Reading Health. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300435Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Mateusz Dolata, Stefan Feuerriegel and Gerhard Schwabe. 2021. A sociotechnical view of algorithmic fairness. Information Systems Journal, 32, 4 (2021), 754-818. https://doi.org/10.1111/isj.12370Google ScholarGoogle ScholarCross RefCross Ref
  32. Philipp Ebel, Matthias Söllner, Jan Marco Leimeister, Kevin Crowston and Gert-Jan de Vreede. 2021. Hybrid intelligence in business networks. Electronic Markets, 31, 2 (2021), 313-318. https://doi.org/10.1007/s12525-021-00481-4Google ScholarGoogle ScholarCross RefCross Ref
  33. Christian Engel, Philipp Ebel and Jan Marco Leimeister. 2022. Cognitive automation. Electronic Markets, 32, 1 (2022), 339-350. https://doi.org/10.1007/s12525-021-00519-7Google ScholarGoogle ScholarCross RefCross Ref
  34. Yoram Eshet-Alkalai. 2004. Digital literacy. Journal of Educational Multimedia and Hypermedia, 13, 1 (2004), 93-106.Google ScholarGoogle Scholar
  35. Susan Folkman. 2013. Stress: Appraisal and Coping. In Encyclopedia of Behavioral Medicine. M. D. Gellman and J. R. Turner (Eds.). Springer. New York.Google ScholarGoogle Scholar
  36. Claes Fornell and David F. Larcker. 1981. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 1 (1981). https://doi.org/10.2307/3151312Google ScholarGoogle ScholarCross RefCross Ref
  37. Andreas Fügener, Jörn Grahl, Alok Gupta and Wolfgang Ketter. 2021. Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research (2021). https://doi.org/10.1287/isre.2021.1079Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Andreas Fügener, Jörn Grahl, Alok Gupta and Wolfgang Ketter. 2021. Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI. MIS Quarterly, 45, 3 (2021), 1527-1556. https://doi.org/10.25300/misq/2021/16553Google ScholarGoogle ScholarCross RefCross Ref
  39. Google. 2022. Advanced Guide to Inception v3. Retrieved July 12, 2022 from https://cloud.google.com/tpu/docs/inception-v3-advancedGoogle ScholarGoogle Scholar
  40. Sophia Hadash, Martijn C. Willemsen, Chris Snijders and Wijnand A. Ijsselsteijn. 2022. Improving understandability of feature contributions in model-agnostic explainable AI tools. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3517650Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch and Jeffrey P. Bigham. 2018. A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk. In Proceedings of the Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3173574.3174023Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Teresa Heyder and Oliver Posegga. 2021. Extending the foundations of AI literacy. In Proceedings of the International Conference on Information Systems. Austin, USA. https://aisel.aisnet.org/icis2021/is_future_work/is_future_work/9Google ScholarGoogle Scholar
  43. Bengt Holmström. 1979. Moral Hazard and Observability. The Bell Journal of Economics, 10, 1 (1979). https://doi.org/10.2307/3003320Google ScholarGoogle ScholarCross RefCross Ref
  44. Kristina Höök. 2000. Steps to take before intelligent user interfaces become real. Interacting with Computers, 12, 4 (2000), 409-426. https://doi.org/10.1016/s0953-5438(99)00006-5Google ScholarGoogle ScholarCross RefCross Ref
  45. Qian Hu, Yaobin Lu, Zhao Pan, Yeming Gong and Zhilin Yang. 2021. Can AI artifacts influence human cognition? The effects of artificial autonomy in intelligent personal assistants. International Journal of Information Management, 56 (2021). https://doi.org/10.1016/j.ijinfomgt.2020.102250Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Jacob Jacoby. 1984. Perspectives on Information Overload. Journal of Consumer Research, 10, 4 (1984). https://doi.org/10.1086/208981Google ScholarGoogle ScholarCross RefCross Ref
  47. Hemant Jain, Balaji Padmanabhan, Paul A. Pavlou and T. S. Raghu. 2021. Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society. Information Systems Research, 32, 3 (2021), 675-687. https://doi.org/10.1287/isre.2021.1046Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Ekaterina Jussupow, Kai Spohrer, Armin Heinzl and Joshua Gawlitza. 2021. Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence. Information Systems Research, 32, 3 (2021), 713-735. https://doi.org/10.1287/isre.2020.0980Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Daniel Kahneman and Amos Tversky. 1984. Choices, values, and frames. American Psychologist, 39, 4 (1984), 341-350. https://doi.org/10.1037/0003-066x.39.4.341Google ScholarGoogle ScholarCross RefCross Ref
  50. Georgi Dimov Kerpedzhiev, Ulrich Matthias König, Maximilian Röglinger and Michael Rosemann. 2020. An Exploration into Future Business Process Management Capabilities in View of Digitalization. Business & Information Systems Engineering, 63, 2 (2020), 83-96. https://doi.org/10.1007/s12599-020-00637-0Google ScholarGoogle ScholarCross RefCross Ref
  51. Alison King. 1992. Comparison of Self-Questioning, Summarizing, and Notetaking-Review as Strategies for Learning From Lectures. American Educational Research Journal, 29, 2 (1992), 303-323. https://doi.org/10.3102/00028312029002303Google ScholarGoogle ScholarCross RefCross Ref
  52. Rafal Kocielnik, Saleema Amershi and Paul N. Bennett. 2019. Will You Accept an Imperfect AI? In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300641Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Vivian Lai, Samuel Carton, Rajat Bhatnagar, Q. Vera Liao, Yunfeng Zhang and Chenhao Tan. 2022. Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3501999Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Richard S. Lazarus and Susan Folkman. 1984. Stress, appraisal and coping. Springer. New YorkGoogle ScholarGoogle Scholar
  55. Carrie R. Leana. 1986. Predictors and Consequences of Delegation. Academy of Management Journal, 29, 4 (1986), 754-774. https://doi.org/10.2307/255943Google ScholarGoogle ScholarCross RefCross Ref
  56. Carrie R. Leana. 1987. Power relinquishment versus power sharing: Theoretical clarification and empirical comparison of delegation and participation. Journal of Applied Psychology, 72, 2 (1987), 228-233. https://doi.org/10.1037/0021-9010.72.2.228Google ScholarGoogle ScholarCross RefCross Ref
  57. Minsun Lee and Jee‐Sun Park. 2022. Do parasocial relationships and the quality of communication with AI shopping chatbots determine middle‐aged women consumers' continuance usage intentions? Journal of Consumer Behaviour, 21, 4 (2022), 842-854. https://doi.org/10.1002/cb.2043Google ScholarGoogle ScholarCross RefCross Ref
  58. Claire Liang, Julia Proft, Erik Andersen and Ross A. Knepper. 2019. Implicit Communication of Actionable Information in Human-AI teams. In Proceedings of the Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300325Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Q. Vera Liao, Daniel Gruen and Sarah Miller. 2020. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3313831.3376590Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Duri Long and Brian Magerko. 2020. What is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu, USA.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Brian Lubars and Chenhao Tan. 2019. Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability. In Proceedings of the 33rd Conference on Neural Information Processing Systems. Vancouver, Canada.Google ScholarGoogle Scholar
  62. Scott B. MacKenzie, Philip M. Podsakoff and Nathan P. Podsakoff. 2011. Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques. MIS Quarterly, 35, 2 (2011). https://doi.org/10.2307/23044045Google ScholarGoogle ScholarCross RefCross Ref
  63. Naresh K. Malhotra. 1982. Information Load and Consumer Decision Making. Journal of Consumer Research, 8, 4 (1982). https://doi.org/10.1086/208882Google ScholarGoogle ScholarCross RefCross Ref
  64. Connor J. McCabe, Dale S. Kim and Kevin M. King. 2018. Improving Present Practices in the Visual Display of Interactions. Adv Methods Pract Psychol Sci, 1, 2 (2018), 147-165. https://doi.org/10.1177/2515245917746792Google ScholarGoogle ScholarCross RefCross Ref
  65. Nathan J. McNeese, Beau G. Schelble, Lorenzo Barberis Canonico and Mustafa Demir. 2021. Who/What Is My Teammate? Team Composition Considerations in Human–AI Teaming. IEEE Transactions on Human-Machine Systems, 51, 4 (2021), 288-299. https://doi.org/10.1109/thms.2021.3086018Google ScholarGoogle ScholarCross RefCross Ref
  66. Patrick Mikalef, Kieran Conboy, Jenny Eriksson Lundström and Aleš Popovič. 2022. Thinking responsibly about responsible AI and ‘the dark side’ of AI. European Journal of Information Systems, 31, 3 (2022), 257-268. https://doi.org/10.1080/0960085x.2022.2026621Google ScholarGoogle ScholarCross RefCross Ref
  67. Allen E. Milewski and Steven H. Lewis. 1997. Delegating to software agents. International Journal of Human-Computer Studies, 46, 4 (1997), 485-500. https://doi.org/10.1006/ijhc.1996.0100Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Franklin G. Moore. 1982. The Management of Organizations. Wiley. New YorkGoogle ScholarGoogle Scholar
  69. Alex Murray, Jen Rhymer and David G. Sirmon. 2021. Humans and Technology: Forms of Conjoined Agency in Organizations. Academy of Management Review, 46, 3 (2021), 552-571. https://doi.org/10.5465/amr.2019.0186Google ScholarGoogle ScholarCross RefCross Ref
  70. Jordan Navarro, Sarah Allali, Nicolas Cabrignac and Julien Cegarra. 2021. Impact of Pilot's Expertise on Selection, Use, Trust, and Acceptance of Automation. IEEE Transactions on Human-Machine Systems, 51, 5 (2021), 432-441. https://doi.org/10.1109/thms.2021.3090199Google ScholarGoogle ScholarCross RefCross Ref
  71. Franz Neyer, Juliane Felber and Claudia Gebhardt. 2016. Kurzskala zur Erfassung von Technikbereitschaft (technology commitment). Zusammenstellung sozialwissenschaftlicher Items und Skalen (ZIS) (2016). https://doi.org/10.6102/zis244Google ScholarGoogle ScholarCross RefCross Ref
  72. Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu and Maggie Shen Qiao. 2021. Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2 (2021). https://doi.org/10.1016/j.caeai.2021.100041Google ScholarGoogle ScholarCross RefCross Ref
  73. Andy Nguyen, Yvonne Hong and Lesley A. Gardner. 2020. A Taxonomy of Digital Learning Activities for Digital Inclusion. In Proceedings of the European Conference on Information Systems. Virtual. https://aisel.aisnet.org/ecis2020_rp/135Google ScholarGoogle Scholar
  74. Tatsuya Nomura, Tomohiro Suzuki, Takayuki Kanda and Kensuke Kato. 2006. Measurement of negative attitudes toward robots. Interaction Studies, 7, 3 (2006), 437-454. https://doi.org/10.1075/is.7.3.14nomGoogle ScholarGoogle ScholarCross RefCross Ref
  75. Ikujiro Nonaka. 1994. A dynamic theory of organizational knowledge creation. Organization Science, 5, 1 (1994), 14-37. http://www.jstor.com/stable/2635068Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. Milda Norkute, Nadja Herger, Leszek Michalak, Andrew Mulder and Sally Gao. 2021. Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization. In Proceedings of the Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411763.3443441Google ScholarGoogle ScholarDigital LibraryDigital Library
  77. Philip M. Podsakoff, Scott B. MacKenzie and Nathan P. Podsakoff. 2016. Recommendations for Creating Better Concept Definitions in the Organizational, Behavioral, and Social Sciences. Organizational Research Methods, 19, 2 (2016), 159-203. https://doi.org/10.1177/1094428115624965Google ScholarGoogle ScholarCross RefCross Ref
  78. Qing Rao and Jelena Frtunikj. 2018. Deep learning for self-driving cars. In Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems. Gothenburg, Sweden.Google ScholarGoogle ScholarDigital LibraryDigital Library
  79. Sebastian Schuetz and Viswanath Venkatesh. 2020. Research Perspectives: The Rise of Human Machines: How Cognitive Computing Systems Challenge Assumptions of User-System Interaction. Journal of the Association for Information Systems (2020), 460-482. https://doi.org/10.17705/1jais.00608Google ScholarGoogle ScholarCross RefCross Ref
  80. Axel Schulte, Diana Donath and Douglas S. Lange. 2016. Design Patterns for Human-Cognitive Agent Teaming. In Engineering Psychology and Cognitive Ergonomics. D. Harris (Ed.). Springer. Cham.Google ScholarGoogle Scholar
  81. Haifeng Shen, Kewen Liao, Zhibin Liao, Job Doornberg, Maoying Qiao, Anton van den Hengel and Johan W. Verjans. 2021. Human-AI Interactive and Continuous Sensemaking: A Case Study of Image Classification using Scribble Attention Maps. In Proceedings of the Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411763.3451798Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. Herbert A. Simon. 1990. Bounded rationality. In Utility and Probability. J. Eatwell, M. Milgate and P. Newman (Eds.). Palgrave Macmillan. London.Google ScholarGoogle Scholar
  83. Ida Someh, Michael Davern, Christoph F. Breidbach and Graeme Shanks. 2019. Ethical Issues in Big Data Analytics: A Stakeholder Perspective. Communications of the Association for Information Systems (2019), 718-747. https://doi.org/10.17705/1cais.04434Google ScholarGoogle ScholarCross RefCross Ref
  84. Stanford Vision Labs. 2022. ImageNet. Retrieved July 12, 2022 from https://www.image-net.org/index.phpGoogle ScholarGoogle Scholar
  85. Frederik Stjernfelt and Anne Mette Lauritzen. 2020. Facebook's Handbook of Content Removal. In Your Post Has Been Removed. Springer. Cham.Google ScholarGoogle Scholar
  86. Yi Sun, Shihui Li and Lingling Yu. 2021. The dark sides of AI personal assistant: effects of service failure on user continuance intention. Electronic Markets, 32, 1 (2021), 17-39. https://doi.org/10.1007/s12525-021-00483-2Google ScholarGoogle ScholarCross RefCross Ref
  87. Thomas Süße, Maria Kobert and Caroline Kries. 2021. Antecedents of Constructive Human-AI Collaboration: An Exploration of Human Actors’ Key Competencies. In Smart and Sustainable Collaborative Networks 4.0. p. 113-124.Google ScholarGoogle Scholar
  88. Monideepa Tarafdar, Xinru Page and Marco Marabelli. 2022. Algorithms as co‐workers: Human algorithm role interactions in algorithmic work. Information Systems Journal (2022). https://doi.org/10.1111/isj.12389Google ScholarGoogle ScholarCross RefCross Ref
  89. Mike Teodorescu, Lily Morse, Yazeed Awwad and Gerald Kane. 2021. Failures of Fairness in Automation Require a Deeper Understanding of Human-ML Augmentation. MIS Quarterly, 45, 3 (2021), 1483-1500. https://doi.org/10.25300/misq/2021/16535Google ScholarGoogle ScholarCross RefCross Ref
  90. Scott Thiebes, Sebastian Lins and Ali Sunyaev. 2020. Trustworthy artificial intelligence. Electronic Markets, 31, 2 (2020), 447-464. https://doi.org/10.1007/s12525-020-00441-4Google ScholarGoogle ScholarCross RefCross Ref
  91. James Y. L. Thong, Se-Joon Hong and Kar Yan Tam. 2006. The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64, 9 (2006), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001Google ScholarGoogle ScholarDigital LibraryDigital Library
  92. Güliz Tokadlı, Michael C. Dorneich and Michael Matessa. 2021. Evaluation of Playbook Delegation Approach in Human-Autonomy Teaming for Single Pilot Operations. International Journal of Human–Computer Interaction, 37, 7 (2021), 703-716. https://doi.org/10.1080/10447318.2021.1890485Google ScholarGoogle ScholarCross RefCross Ref
  93. Eric J. Topol. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 25, 1 (Jan 2019), 44-56. https://doi.org/10.1038/s41591-018-0300-7Google ScholarGoogle ScholarCross RefCross Ref
  94. P. Tschandl, C. Rinner, Z. Apalla, G. Argenziano, N. Codella, A. Halpern, M. Janda, A. Lallas, C. Longo, J. Malvehy, J. Paoli, S. Puig, C. Rosendahl, H. P. Soyer, I. Zalaudek and H. Kittler. 2020. Human-computer collaboration for skin cancer recognition. Nat Med, 26, 8 (2020), 1229-1234. https://doi.org/10.1038/s41591-020-0942-0Google ScholarGoogle ScholarCross RefCross Ref
  95. Amos Tversky and Daniel Kahneman. 1974. Judgment under Uncertainty: Heuristics and Biases. Science, 185, 4157 (1974), 1124–1131. http://www.jstor.org/stable/1738360Google ScholarGoogle Scholar
  96. Benjamin van Giffen, Dennis Herhausen and Tobias Fahse. 2022. Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, 144 (2022), 93-106. https://doi.org/10.1016/j.jbusres.2022.01.076Google ScholarGoogle ScholarCross RefCross Ref
  97. Jhim Kiel M. Verame, Enrico Costanza, Joel Fischer, Andy Crabtree, Sarvapali D. Ramchurn, Tom Rodden and Nicholas R. Jennings. 2018. Learning from the Veg Box. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3173574.3174021Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. Michael Vössing, Niklas Kühl, Matteo Lind and Gerhard Satzger. 2022. Designing Transparency for Effective Human-AI Collaboration. Information Systems Frontiers, 24, 3 (2022), 877-895. https://doi.org/10.1007/s10796-022-10284-3Google ScholarGoogle ScholarDigital LibraryDigital Library
  99. Bingcheng Wang, Pei-Luen Patrick Rau and Tianyi Yuan. 2022. Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology (2022), 1-14. https://doi.org/10.1080/0144929x.2022.2072768Google ScholarGoogle ScholarCross RefCross Ref
  100. Danding Wang, Qian Yang, Ashraf Abdul and Brian Y. Lim. 2019. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300831Google ScholarGoogle ScholarDigital LibraryDigital Library
  101. Leslie Willcocks. 2020. Robo-Apocalypse cancelled? Reframing the automation and future of work debate. Journal of Information Technology, 35, 4 (2020), 286-302. https://doi.org/10.1177/0268396220925830Google ScholarGoogle ScholarCross RefCross Ref
  102. Michael Williams, Hassen Gharbi, Aybike Ulusan, Ozlem Ergun, Zhu Xiaofeng, Shiyu Zhang and Casper Harteveld. 2016. Toward Human in the Loop Optimization Through Game-Based Experiments. In Proceedings of the Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts. https://doi.org/10.1145/2968120.2987733Google ScholarGoogle ScholarDigital LibraryDigital Library
  103. World Economic Forum. 2022. Without universal AI literacy, AI will fail us. Retrieved August 10, 2022 from https://www.weforum.org/agenda/2022/03/without-universal-ai-literacy-ai-will-fail-us/Google ScholarGoogle Scholar
  104. Y. Xie, I.P. Bodala, D. C. Ong, D. Hsu and H. Soh. 2019. Robot Capability and Intention in Trust-Based Decisions Across Tasks. In Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI).Google ScholarGoogle Scholar
  105. Su-Fang Yeh, Meng-Hsin Wu, Tze-Yu Chen, Yen-Chun Lin, XiJing Chang, You-Hsuan Chiang and Yung-Ju Chang. 2022. How to Guide Task-oriented Chatbot Users, and When: A Mixed-methods Study of Combinations of Chatbot Guidance Types and Timings. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3501941Google ScholarGoogle ScholarDigital LibraryDigital Library
  106. Sangseok You, Cathy Liu Yang and Xitong Li. 2022. Algorithmic versus Human Advice: Does Presenting Prediction Performance Matter for Algorithm Appreciation? Journal of Management Information Systems, 39, 2 (2022), 336-365. https://doi.org/10.1080/07421222.2022.2063553Google ScholarGoogle ScholarCross RefCross Ref
  107. Gary Yukl and Ping P. Fu. 1999. Determinants of Delegation and Consultation by Managers. Journal of Organizational Behavior, 20, 2 (1999), 219-232. https://www.jstor.org/stable/3100422Google ScholarGoogle ScholarCross RefCross Ref
  108. Jichen Zhu, Jennifer Villareale, Nithesh Javvaji, Sebastian Risi, Mathias Löwe, Rush Weigelt and Casper Harteveld. 2021. Player-AI Interaction: What Neural Network Games Reveal About AI as Play. In Proceedings of the Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411764.3445307Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. AI Knowledge: Improving AI Delegation through Human Enablement

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
          April 2023
          14911 pages
          ISBN:9781450394215
          DOI:10.1145/3544548

          Copyright © 2023 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 19 April 2023

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited

          Acceptance Rates

          Overall Acceptance Rate6,199of26,314submissions,24%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format