A Decision Theoretic Framework for Measuring AI Reliance

• URL: http://arxiv.org/abs/2401.15356v4
• Date: Sun, 12 May 2024 19:33:49 GMT
• Title: A Decision Theoretic Framework for Measuring AI Reliance
• Authors: Ziyang Guo, Yifan Wu, Jason Hartline, Jessica Hullman,
• Abstract summary: Humans frequently make decisions with the aid of artificially intelligent (AI) systems. Researchers have identified ensuring that a human has appropriate reliance on an AI as a critical component of achieving complementary performance. 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.
• Score: 23.353778024330165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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