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.

翻译：为了确保人类与采用机器学习进行决策的智能体之间有效协作，人类必须理解智能体能够和不能完成的任务，以避免过度或不充分依赖。解决这一问题的途径是通过使用依赖校准线索（RCCs）进行沟通来调节人类依赖，帮助人类评估智能体的能力。以往研究通常通过持续呈现RCCs来校准依赖，但智能体应在何时提供RCCs仍是一个未解决的问题。为回答这一问题，我们提出Pred-RC方法，用于选择性提供RCCs。Pred-RC利用认知依赖模型预测人类是否会将任务分配给智能体。通过比较有无RCC两种情况下的人类依赖预测结果，Pred-RC评估RCC对依赖行为的影响。我们在人机协作任务中测试了Pred-RC，结果表明该方法能够以更少的RCCs成功校准人类依赖。