The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.

翻译：人机协作的真正潜力在于利用人类与AI的互补能力，实现超越单独AI或人类的联合绩效，即达成互补团队绩效（CTP）。为了实现这种互补潜力，人类需要谨慎地决定是否遵循AI的建议，即恰当依赖AI的建议。虽然以往研究侧重于构建AI的心智模型以评估AI推荐，但最新研究表明，仅凭心智模型无法解释恰当的依赖行为。我们假设，除心智模型外，人类学习是促成恰当依赖进而实现CTP的关键中介因素。本研究通过一项包含100名参与者的实验，论证了学习与恰当依赖之间的关系。该工作为分析依赖行为提供了基础概念，并为人机决策的有效设计提供了启示。