AI RESEARCH
Learning to Trust: How Humans Mentally Recalibrate AI Confidence Signals
arXiv CS.AI
•
ArXi:2603.22634v1 Announce Type: cross Productive human-AI collaboration requires appropriate reliance, yet contemporary AI systems are often miscalibrated, exhibiting systematic overconfidence or underconfidence. We investigate whether humans can learn to mentally recalibrate AI confidence signals through repeated experience. In a behavioral experiment (N = 200), participants predicted the AI's correctness across four AI calibration conditions: standard, overconfidence, underconfidence, and a counterintuitive "reverse confidence" mapping.