发表机构
Cornell University; McMaster University（康奈尔大学; 麦克马斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过实验发现，在无外部反馈的AI辅助决策中，解释会增加新手过度依赖，而自我评估有助于校准依赖，为设计支持适当依赖的AI工具提供指导。
AI 中文摘要
人工智能（AI）工具被广泛用于支持在无法获得即时性能反馈的任务和领域中的决策。在这些情境下，用户无法通过试错来学习随时间调整其依赖行为。然而，关于当外部反馈不可用时，新手用户如何校准对AI的依赖，或者AI解释能否在缺乏反馈的情况下支持校准，目前知之甚少。我们引入依赖校准作为一个组织性概念，用于研究新手用户如何动态调整依赖行为，并考察AI解释和元认知自我评估如何塑造这一过程。通过一项包含110名参与者的受试者间研究，参与者在AI辅助和有限性能反馈下完成临床实体提取任务，我们观察到，在存在解释的情况下，新手用户表现出系统性向过度依赖的漂移，而较高的自我报告任务理解度与更具选择性的依赖行为相关。这些结果将依赖校准研究扩展到没有实时性能信号的人机协作情境中，并为设计必须在此类情境中支持适当依赖的AI工具提供了可操作的指导方针。
英文摘要
Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is available. In these settings, users cannot learn to adjust their reliance behavior over time through trial and error. However, little is known about how novice users calibrate reliance on AI when external feedback is unavailable, or whether AI explanations can support calibration in its absence. We introduce reliance calibration as an organizing construct for studying how novice users dynamically adjust reliance behavior, and examine how AI explanations and meta-cognitive self-assessment shape it. Through a between-subjects study with 110 participants completing a clinical entity extraction task with AI assistance and limited performance feedback, we observe that novice users exhibit systematic drift toward over-reliance in the presence of explanations, while higher self-reported task understanding is associated with more selective reliance behavior. These results extend reliance calibration research into human-AI collaboration contexts without real-time performance signals and present actionable guidelines on designing AI tools that must support appropriate reliance in these settings.
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