In AI-assisted decision-making, a central promise of putting a human in the loop is that they should be able to complement the AI system by adhering to its correct and overriding its mistaken recommendations. In practice, however, we often see that humans tend to over- or under-rely on AI recommendations, meaning that they either adhere to wrong or override correct recommendations. Such reliance behavior is detrimental to decision-making accuracy. In this work, we articulate and analyze the interdependence between reliance behavior and accuracy in AI-assisted decision-making, which has been largely neglected in prior work. We also propose a visual framework to make this interdependence more tangible. This framework helps us interpret and compare empirical findings, as well as obtain a nuanced understanding of the effects of interventions (e.g., explanations) in AI-assisted decision-making. Finally, we infer several interesting properties from the framework: (i) when humans under-rely on AI recommendations, there may be no possibility for them to complement the AI in terms of decision-making accuracy; (ii) when humans cannot discern correct and wrong AI recommendations, no such improvement can be expected either; (iii) interventions may lead to an increase in decision-making accuracy that is solely driven by an increase in humans' adherence to AI recommendations, without any ability to discern correct and wrong. Our work emphasizes the importance of measuring and reporting both effects on accuracy and reliance behavior when empirically assessing interventions.

翻译：在AI辅助决策中，将人类置于决策循环的核心承诺之一是，人类应能通过遵循AI正确建议并推翻其错误推荐来补充AI系统。然而实际中，我们常观察到人类倾向于过度依赖或依赖不足于AI推荐，即要么遵循错误建议，要么推翻正确建议。这种依赖行为会损害决策准确性。本研究阐述并分析了AI辅助决策中依赖行为与准确性之间的相互依存关系——这一关系在先前工作中常被忽视。我们还提出一个可视化框架，使这种相互依存性更具可感知性。该框架有助于我们解释和比较实证发现，并对AI辅助决策中干预措施（如解释）的影响获得细致理解。最终，我们从该框架推导出若干有趣性质：（i）当人类对AI推荐依赖不足时，他们可能无法在决策准确性方面补充AI；（ii）当人类无法区分AI推荐的对错时，同样无法预期此类改进；（iii）干预措施可能导致决策准确性的提升，这种提升完全源于人类对AI推荐遵循度的增加，而无需具备区分对错的能力。本工作强调，在实证评估干预措施时，同时测量和报告准确性与依赖行为两方面影响的重要性。