AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this advice is typically imperfect, decision-makers have to exert discretion as to whether actually follow that advice: they have to "appropriately" rely on correct and turn down incorrect advice. However, current research on appropriate reliance still lacks a common definition as well as an operational measurement concept. Additionally, no in-depth behavioral experiments have been conducted that help understand the factors influencing this behavior. In this paper, we propose Appropriateness of Reliance (AoR) as an underlying, quantifiable two-dimensional measurement concept. We develop a research model that analyzes the effect of providing explanations for AI advice. In an experiment with 200 participants, we demonstrate how these explanations influence the AoR, and, thus, the effectiveness of AI advice. Our work contributes fundamental concepts for the analysis of reliance behavior and the purposeful design of AI advisors.

翻译：AI建议在投资和医疗决策等领域日益普及。由于这些建议通常并不完美，决策者需要自行判断是否采纳这些建议：他们需要“适度地”依赖正确建议并拒绝错误建议。然而，当前关于适度依赖的研究仍缺乏统一定义和可操作化的测量概念。此外，尚未开展深入的实验研究以理解影响该行为的因素。本文提出“依赖适度性”（Appropriateness of Reliance, AoR）作为基础性的可量化二维测量概念，并构建研究模型分析AI建议解释机制的影响。通过200名参与者的实验，我们证明了这些解释如何影响AoR及其对AI建议有效性的作用。本研究为依赖行为分析和AI顾问的针对性设计提供了基础性概念框架。