Humans frequently make decisions with the aid of artificially intelligent (AI) systems. A common pattern is for the AI to recommend an action to the human who retains control over the final decision. Researchers have identified ensuring that a human has appropriate reliance on an AI as a critical component of achieving complementary performance. We argue that the current definition of appropriate reliance used in such research lacks formal statistical grounding and can lead to contradictions. We propose a formal definition of reliance, based on statistical decision theory, which separates the concepts of reliance as the probability the decision-maker follows the AI's recommendation from challenges a human may face in differentiating the signals and forming accurate beliefs about the situation. Our definition gives rise to a framework that can be used to guide the design and interpretation of studies on human-AI complementarity and reliance. Using recent AI-advised decision making studies from literature, we demonstrate how our framework can be used to separate the loss due to mis-reliance from the loss due to not accurately differentiating the signals. We evaluate these losses by comparing to a baseline and a benchmark for complementary performance defined by the expected payoff achieved by a rational decision-maker facing the same decision task as the behavioral decision-makers.

翻译：人类在决策时经常借助人工智能（AI）系统。常见的模式是AI向保留最终决策权的人类推荐行动方案。研究人员已指出，确保人类对AI具有适当依赖是实现互补性能的关键。我们认为，当前此类研究中使用的"适当依赖"定义缺乏正式统计基础，且可能导致矛盾。基于统计决策理论，我们提出了依赖的正式定义，将"依赖"（即决策者遵循AI建议的概率）与人类在区分信号、形成对情境的准确认知方面可能面临的挑战这两个概念相分离。该定义衍生出一个框架，可用于指导人类-AI互补性与依赖相关研究的设计与解读。通过运用文献中基于AI辅助决策的最新研究案例，我们展示了如何利用该框架将因错误依赖导致的损失与未能准确区分信号造成的损失加以区分。我们通过对比基线及互补性能基准（由行为决策者执行相同决策任务的理性决策者所实现的期望收益来定义）来评估这些损失。