Personalising AI Assistance Based on Overreliance Rate in AI-Assisted Decision Making
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Swaroop et al. (2025) showed that adapting the type of AI assistance based on each user's observed overreliance rate improves human-AI team performance compared to uniform assistance.
“Swaroop et al. (2025) showed that adapting the type of AI assistance based on each user's observed overreliance rate (providing higher-friction support to users who tend to accept AI suggestions uncritically, and lower-friction support to well-calibrated users) improves human-AI team performance compared to uniform assistance.”
From From Oracles to Productive Friction: Designing for Meaningful Human Oversight in Clinical AI (2026) · cites this paper as Swaroop et al. (2025)
Cites
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124 · see citing works
Self-citationZhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372852 · Cited by 895 · see citing works
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757 · see citing works
The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems
Bussone, A., Stumpf, S., & O'Sullivan, D. (2015) · 2015 International Conference on Healthcare Informatics · doi:10.1109/ichi.2015.26 · Cited by 441 · see citing works
Explanations Can Reduce Overreliance on AI Systems During Decision-Making
Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., & Krishna, R. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579605 · Cited by 402 · see citing works
The Principles and Limits of Algorithm-in-the-Loop Decision Making
Green, B., & Chen, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359152 · Cited by 392 · cited 2× · see citing works
“However, using a fxed type of AI assistance for all people does not lead to complementarity [3, 6, 10, 19, 36, 51].”
Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377 · see citing works
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 · see citing works
Jacobs, M., Pradier, M. F., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01224-x · Cited by 247 · see citing works
Self-citationMiller, T. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594001 · Cited by 172 · see citing works
Sivaraman, V., Bukowski, L. A., Levin, J., Kahn, J. M., & Perer, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581075 · Cited by 141 · see citing works
Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
Gajos, K. Z., & Mamykina, L. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511138 · Cited by 127 · see citing works
Self-citationRundo, L., Pirrone, R., Vitabile, S., Sala, E., & Gambino, O. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103479 · Cited by 113 · see citing works
Levy, A., Agrawal, M., Satyanarayan, A., & Sontag, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445522 · Cited by 73 · see citing works
Zhang, Z. T., Buchner, F., Liu, Y., & Butz, A. (2024) · Proceedings of Mensch und Computer 2024 · doi:10.1145/3670653.3670660 · Cited by 6 · see citing works
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Raees, M., Khan, V.-J., Lykourentzou, I., & Papangelis, K. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791467 · Cited by 5 · see citing works
From Oracles to Productive Friction: Designing for Meaningful Human Oversight in Clinical AI
Rubegni, E., Natali, C., Valsecchi, C., Rizzo, S., Ayoub, O., & Facchini, A. (2026) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v9i3.43300
- Authors
- Swaroop, S., Buçinca, Z., Gajos, K. Z., & Doshi-Velez, F.
- Affiliations
- Harvard University (US)
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA,
- Venue
- Proceedings of the 30th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2025-03-24
- Pages
- 1107-1122
- OpenAlex
- W4408589359
- Cited by
- 20
- References
- 15
- Citations by year
- 2026: 14 · 2025: 6
- FWCI
- 38.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Funders
- National Science Foundation (IIS-2107391)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- AI-assisted decision-making, overreliance on AI, personalization, human-AI interaction, trust in AI, decision support
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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