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建议越来越受欢迎,例如在投资和医疗决定方面。由于这种建议通常不完善,决策者必须斟酌决定是否实际遵循这种建议:他们必须 " 适当 " 依赖正确的建议,拒绝不正确的建议。然而,目前关于适当依赖的研究仍然缺乏共同的定义和实用的衡量概念。此外,没有进行深入的行为实验,帮助理解影响这种行为的因素。在本文件中,我们提出 " 依赖的适当性 " 是一个基本、可量化的二维衡量概念。我们开发了一个研究模型,分析为AI建议提供解释的效果。在与200名参与者进行的试验中,我们展示这些解释如何影响AOR,从而也缺乏AI建议的有效性。我们的工作为分析依赖行为和AI顾问的有目的设计提供了基本概念。