  In AI-assisted decision-making, it is critical for human decision-makers to
know when to trust AI and when to trust themselves. However, prior studies
calibrated human trust only based on AI confidence indicating AI's correctness
likelihood (CL) but ignored humans' CL, hindering optimal team decision-making.
To mitigate this gap, we proposed to promote humans' appropriate trust based on
the CL of both sides at a task-instance level. We first modeled humans' CL by
approximating their decision-making models and computing their potential
performance in similar instances. We demonstrated the feasibility and
effectiveness of our model via two preliminary studies. Then, we proposed three
CL exploitation strategies to calibrate users' trust explicitly/implicitly in
the AI-assisted decision-making process. Results from a between-subjects
experiment (N=293) showed that our CL exploitation strategies promoted more
appropriate human trust in AI, compared with only using AI confidence. We
further provided practical implications for more human-compatible AI-assisted
decision-making.
