"Are You Really Sure?" Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making
Proceedings of the CHI Conference on Human Factors in Computing Systems(2024)
摘要
In AI-assisted decision-making, it is crucial but challenging for humans to
achieve appropriate reliance on AI. This paper approaches this problem from a
human-centered perspective, "human self-confidence calibration". We begin by
proposing an analytical framework to highlight the importance of calibrated
human self-confidence. In our first study, we explore the relationship between
human self-confidence appropriateness and reliance appropriateness. Then in our
second study, We propose three calibration mechanisms and compare their effects
on humans' self-confidence and user experience. Subsequently, our third study
investigates the effects of self-confidence calibration on AI-assisted
decision-making. Results show that calibrating human self-confidence enhances
human-AI team performance and encourages more rational reliance on AI (in some
aspects) compared to uncalibrated baselines. Finally, we discuss our main
findings and provide implications for designing future AI-assisted
decision-making interfaces.
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