  For effective collaboration between humans and intelligent agents that employ
machine learning for decision-making, humans must understand what agents can
and cannot do to avoid over/under-reliance. A solution to this problem is
adjusting human reliance through communication using reliance calibration cues
(RCCs) to help humans assess agents' capabilities. Previous studies typically
attempted to calibrate reliance by continuously presenting RCCs, and when an
agent should provide RCCs remains an open question. To answer this, we propose
Pred-RC, a method for selectively providing RCCs. Pred-RC uses a cognitive
reliance model to predict whether a human will assign a task to an agent. By
comparing the prediction results for both cases with and without an RCC,
Pred-RC evaluates the influence of the RCC on human reliance. We tested Pred-RC
in a human-AI collaboration task and found that it can successfully calibrate
human reliance with a reduced number of RCCs.
