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：为实现人类与采用机器学习进行决策的智能体之间的有效协作，人类必须了解智能体所能及所不能之事，以避免过度依赖或依赖不足。解决该问题的一种途径是通过使用依赖校准线索（RCCs）进行沟通来调整人类依赖，以帮助人类评估智能体的能力。以往研究通常尝试通过持续呈现RCCs来校准依赖，但关于智能体应在何时提供RCCs仍是一个开放性问题。为回答该问题，我们提出Pred-RC——一种选择性提供RCCs的方法。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两种情形下的预测结果，Pred-RC评估RCC对人类依赖的影响。我们在人机协作任务中测试了Pred-RC，发现该方法能以更少的RCCs成功校准人类依赖。