The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI
Open accessAbstract
We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: “unique human knowledge.” In a multi-method study consisting of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accuracy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like “Borgs,” that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human–AI decision environments. We demonstrate this harmful impact on the “wisdom of crowds.” Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments that can provide the best of both worlds (e.g., by personalizing AI advice). We show that such interventions perform well individually as well as in wisdom of crowds settings.
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Cites
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
Statistical Power Analysis for the Behavioral Sciences
Cohen, J. (2013) · doi:10.4324/9780203771587 · Cited by 25,373
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
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
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Groups of diverse problem solvers can outperform groups of high-ability problem solvers
Hong, L., & Page, S. E. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403723101 · Cited by 2,103
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
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018) · Nature · doi:10.1038/s41586-018-0637-6 · Cited by 1,859
GALTON, F. (1907) · Nature · doi:10.1038/075450a0 · Cited by 1,080
How social influence can undermine the wisdom of crowd effect
Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008636108 · Cited by 1,017
Bonaccio, S., & Dalal, R. S. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.07.001 · Cited by 1,011
Wolfers, J., & Zitzewitz, E. (2004) · Journal of Economic Perspectives · doi:10.1257/0895330041371321 · Cited by 889
Foster, E. D., & Deardorff, A. (2017) · Journal of the Medical Library Association · doi:10.5195/jmla.2017.88 · Cited by 715
Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance
Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5285 · Cited by 466
Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377
Paulus, P. B., & Brown, V. R. (2007) · Social and Personality Psychology Compass · doi:10.1111/j.1751-9004.2007.00006.x · Cited by 364
Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff
Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33012429 · Cited by 284 · cited 6×
“Possible applications are discussed in Bansal et al. (2019a) and include medical decision making (e.g., Bayati et al. 2014 on the prediction of readmission rates to support physicians) or legal support (e.g., Angwin et al. 2016 on recidivism prediction to support judges).”
BackgroundInfluential"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Lai, V., Liu, H., & Tan, C. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376873 · Cited by 123
Bayati, M., Braverman, M., Gillam, M., Mack, K. M., Ruiz, G., Smith, M. S., & Horvitz, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109264 · Cited by 122 · cited 1×
“Possible applications are discussed in Bansal et al. (2019a) and include medical decision making (e.g., Bayati et al. 2014 on the prediction of readmission rates to support physicians) or legal support (e.g., Angwin et al. 2016 on recidivism prediction to support judges).”
BackgroundFeature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity
Carton, S., Mei, Q., & Resnick, P. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7282 · Cited by 58
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Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2023.2233814 · Cited by 1,363
Ooi, K.-B., Tan, G. W.-H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kar, A. K., Lee, V.-H., et al. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2261010 · Cited by 746
Managing artificial intelligence
Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16274 · Cited by 898
Thinking responsibly about responsible AI and ‘the dark side’ of AI
Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2026621 · Cited by 435
Unveiling the shadows: beyond the hype of AI in education
Al-Zahrani, A. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30696 · Cited by 225
Metadata
- Authors
- Fügener, A., Grahl, J., Gupta, A., & Ketter, W.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2021-09-01
- Volume
- 45
- Issue
- 3
- Pages
- 1527-1556
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W3201195645
- Cited by
- 303
- References
- 20
- FWCI
- 30.22
- Type
- journal article
- Language
- en
- Open access
- green
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