  AI transparency is a central pillar of responsible AI deployment and
effective human-AI collaboration. A critical approach is communicating
uncertainty, such as displaying AI's confidence level, or its correctness
likelihood (CL), to users. However, these confidence levels are often
uncalibrated, either overestimating or underestimating actual CL, posing risks
and harms to human-AI collaboration. This study examines the effects of
uncalibrated AI confidence on users' trust in AI, AI advice adoption, and
collaboration outcomes. We further examined the impact of increased
transparency, achieved through trust calibration support, on these outcomes.
Our results reveal that uncalibrated AI confidence leads to both the misuse of
overconfident AI and disuse of unconfident AI, thereby hindering outcomes of
human-AI collaboration. Deficiency of trust calibration support exacerbates
this issue by making it harder to detect uncalibrated confidence, promoting
misuse and disuse of AI. Conversely, trust calibration support aids in
recognizing uncalibration and reducing misuse, but it also fosters distrust and
causes disuse of AI. Our findings highlight the importance of AI confidence
calibration for enhancing human-AI collaboration and suggest directions for AI
design and regulation.
