AI依赖与决策质量：基本原理、相互依赖关系及干预措施的效果
AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions
AI总结：
本文厘清并形式化了AI辅助决策中依赖行为与决策质量的关系，提出可视化框架以刻画人机互补条件，并用于解释干预措施效果，强调区分二者对研究与实践的重要性。
摘要：
在AI辅助决策中，人在回路的一个核心承诺是，人类应能通过推翻AI的错误建议来补充AI系统。然而在实践中，我们经常看到人类无法评估AI建议的正确性，因而遵从错误建议或推翻正确建议。依赖AI建议的不同方式对决策质量有着直接但截然不同的影响。遗憾的是，在当前关于AI辅助决策的文献中，依赖与决策质量常常被不恰当地混为一谈。在这项工作中，我们厘清并形式化了依赖与决策质量之间的关系，并刻画了可实现人机互补的条件。为了说明依赖与决策质量如何相互关联，我们提出了一个可视化框架，并展示了其在解释实证发现（包括解释等干预措施的效果）方面的实用性。总体而言，我们的研究强调了在AI辅助决策中区分依赖行为与决策质量的重要性。
英文摘要：
In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In practice, however, we often see that humans cannot assess the correctness of AI recommendations and, as a result, adhere to wrong or override correct advice. Different ways of relying on AI recommendations have immediate, yet distinct, implications for decision quality. Unfortunately, reliance and decision quality are often inappropriately conflated in the current literature on AI-assisted decision-making. In this work, we disentangle and formalize the relationship between reliance and decision quality, and we characterize the conditions under which human-AI complementarity is achievable. To illustrate how reliance and decision quality relate to one another, we propose a visual framework and demonstrate its usefulness for interpreting empirical findings, including the effects of interventions like explanations. Overall, our research highlights the importance of distinguishing between reliance behavior and decision quality in AI-assisted decision-making.
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。
