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：人工智能建议在投资和医疗决策等领域日益普及。由于此类建议通常并不完美，决策者需自行斟酌是否实际采纳该建议：他们必须“适当”地依赖正确建议而拒绝错误建议。然而，当前关于适当依赖的研究仍缺乏统一定义及可操作化的测量概念。此外，尚未开展有助于理解影响该行为因素的深度行为实验。本文提出“依赖适当性”（Appropriateness of Reliance, AoR）作为一个基础性的、可量化的二维测量概念。我们构建了一个研究模型，用于分析提供人工智能建议解释的效果。通过一项包含200名参与者的实验，我们验证了这些解释如何影响依赖适当性，进而影响人工智能建议的有效性。本研究为依赖行为分析及人工智能顾问的有目的设计提供了基础性概念。