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 prediction 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：人类时常在人工智能系统的辅助下做出决策。常见模式是，人工智能向保留最终决策权的人类推荐行动方案。研究者已认识到，确保人类对人工智能保持适当依赖是实现互补性能的关键要素。我们认为，当前此类研究中采用的“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，我们提出了一种形式化的依赖定义，该定义将依赖概念（即决策者遵循人工智能预测的概率）与人类在区分信号和形成准确情境认知时可能面临的挑战相分离。这一定义衍生出一个框架，可用于指导人机互补性与依赖性研究的设计与解读。通过文献中近期的人工智能辅助决策研究案例，我们展示了该框架如何将因错误依赖导致的损失与因未能准确区分信号导致的损失相分离。通过将行为决策者所面对相同决策任务中的理性决策者预期收益作为互补性能的基准线进行对比，我们评估了这些损失。