The current literature on AI-advised decision making -- involving explainable AI systems advising human decision makers -- presents a series of inconclusive and confounding results. To synthesize these findings, we propose a simple theory that elucidates the frequent failure of AI explanations to engender appropriate reliance and complementary decision making performance. We argue explanations are only useful to the extent that they allow a human decision maker to verify the correctness of an AI's prediction, in contrast to other desiderata, e.g., interpretability or spelling out the AI's reasoning process. Prior studies find in many decision making contexts AI explanations do not facilitate such verification. Moreover, most tasks fundamentally do not allow easy verification, regardless of explanation method, limiting the potential benefit of any type of explanation. We also compare the objective of complementary performance with that of appropriate reliance, decomposing the latter into the notions of outcome-graded and strategy-graded reliance.

翻译：当前关于AI辅助决策（即由可解释AI系统为人类决策者提供建议）的文献呈现出一系列尚无定论且令人困惑的研究结果。为综合这些发现，我们提出一个简单理论，旨在阐释AI解释为何常无法引发恰当依赖及互补性决策性能。我们主张，解释仅在其允许人类决策者验证AI预测正确性的程度上才有价值，而非满足其他期望属性，例如可解释性或详述AI推理过程。先前研究发现，在许多决策情境下，AI解释并无法促进此类验证。此外，无论采用何种解释方法，大多数任务本质上也难以实现简单验证，从而限制了各类解释的潜在益处。我们还比较了互补性能与恰当依赖这两个目标，并将后者分解为结果导向依赖与策略导向依赖这两个概念。