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Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation
Open accessAbstract
A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
The Skill Content of Recent Technological Change: An Empirical Exploration
Autor, D. H., Levy, F., & Murnane, R. J. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552801 · Cited by 6,205
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232
Algorithm appreciation: People prefer algorithmic to human judgment
Logg, J. M., Minson, J. A., & Moore, D. A. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2018.12.005 · Cited by 2,061
The preregistration revolution
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708274114 · Cited by 1,998
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018) · Management Science · doi:10.1287/mnsc.2016.2643 · Cited by 1,230
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Chen, Z., & Chan, J. (2024) · Management Science · doi:10.1287/mnsc.2023.03014 · Cited by 204
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Dhillon, P. S., Molaei, S., Li, J., Golub, M., Zheng, S., & Robert, L. P. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642134 · Cited by 111
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Pinski, M., & Benlian, A. (2023) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2023.021 · Cited by 94
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Human–artificial intelligence collaboration in prediction: a field experiment in the retail industry
Revilla, E., Saenz, M. J., Seifert, M., & Ma, Y. (2023) · Journal of Management Information Systems · doi:10.1080/07421222.2023.2267317 · Cited by 63
Yang, J., Amrollahi, A., & Marrone, M. (2024) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2024.101864 · Cited by 59
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Krakowski, S., Haftor, D., Luger, J., Pashkevich, N., & Raisch, S. (2026) · Management Science · doi:10.1287/mnsc.2022.03849 · Cited by 53
Democratizing Knowledge Creation Through Human-AI Collaboration in Academic Peer Review
Sarker, S., University of Virginia, Susarla, A., Michigan State University, Gopal, R., University of Warwick, Thatcher, J. B., & University of Colorado / University of Manchester (2024) · Journal of the Association for Information Systems · doi:10.17705/1jais.00872 · Cited by 25
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Spitzer, P., Holstein, J., Hemmer, P., Vössing, M., Kühl, N., Martin, D., & Satzger, G. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710999 · Cited by 18
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Sunyaev, A., Benlian, A., Pfeiffer, J., Jussupow, E., Thiebes, S., Maedche, A., & Gawlitza, J. (2025) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00942-6 · Cited by 16
Herath, S., Bashardoust, A., Bole, Y., & Shrestha, Y. R. (2026) · European Journal of Information Systems · doi:10.1080/0960085x.2026.2616042 · Cited by 16
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Metadata
- Authors
- Fügener, A., Grahl, J., Gupta, A., & Ketter, W.
- Venue
- Information Systems Research
- Published
- 2022-06
- Volume
- 33
- Issue
- 2
- Pages
- 678-696
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W4200195738
- Cited by
- 478
- References
- 16
- FWCI
- 39.43
- Type
- journal article
- Language
- en
- Open access
- green
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