As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

要旨

Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' self-confidence and its calibration. To explore these dynamics, we conducted a randomized behavioral experiment. Our results indicate that in human-AI decision-making, users' self-confidence aligns with AI confidence and such alignment can persist even after AI ceases to be involved. This alignment then affects users' self-confidence calibration. We also found the presence of real-time correctness feedback of decisions reduced the degree of alignment. These findings suggest that users' self-confidence is not independent of AI confidence, which practitioners aiming to achieve better human-AI collaboration need to be aware of. We call for research focusing on the alignment of human cognition and behavior with AI.

受賞
Honorable Mention
著者
Jingshu Li
National University of Singapore, Singapore, Singapore
Yitian Yang
National University of Singapore, Singapore, Singapore
Q. Vera Liao
Microsoft Research, Montreal, Quebec, Canada
Junti Zhang
National University of Singapore, Singapore, Singapore
YI-CHIEH LEE
National University of Singapore, Singapore, Singapore
DOI

10.1145/3706598.3713336

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713336

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Understanding and Working with Algorithms

Annex Hall F206
6 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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