对AI建议的适当依赖：概念化与解释的作用
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations
AI总结：
本文提出可量化的二维“依赖适当性”（AoR）概念，并通过200人实验证明为AI建议提供解释能够影响用户对AI的适当依赖，从而提升AI建议的有效性。
摘要：
AI建议正变得越来越流行，例如在投资和医疗治疗决策中。由于这类建议通常并不完美，决策者必须自行判断是否真正遵循该建议：他们必须“适当地”依赖正确的建议，并拒绝错误的建议。然而，当前关于适当依赖的研究仍缺乏一个共同的定义以及可操作的测量概念。此外，尚未开展深入的受控行为实验来帮助理解影响这种行为的因素。在本文中，我们提出“依赖适当性”（AoR）作为一个基础的、可量化的二维测量概念。我们开发了一个研究模型，用于分析为AI建议提供解释所产生的效果。在一项有200名参与者参加的实验中，我们展示了这些解释如何影响AoR，进而影响AI建议的有效性。我们的工作为分析依赖行为和有针对性地设计AI顾问提供了基础性概念。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
