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的应用日益普及。由于此类建议通常存在缺陷，决策者需自行判断是否采纳这些建议：即需"适度"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及操作性测量概念，且尚无深入的实验研究帮助理解影响该行为的因素。本文提出"依赖适度性"（Appropriateness of Reliance, AoR）作为可量化的二维测量概念，并构建研究模型分析人工智能建议解释效果的影响。通过对200名参与者的实验，我们揭示了这些解释如何影响依赖适度性，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定向设计提供了基础性概念。