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辅助决策的文献（涉及可解释AI系统为人类决策者提供建议）呈现出一系列不确定且令人困惑的结果。为综合这些发现，我们提出一个简明理论，阐明AI解释为何经常无法引发适当的依赖和互补性决策性能。我们认为，解释仅在其能让人类决策者验证AI预测正确性时才有用，而非其他目标（如可解释性或阐明AI推理过程）。先前研究发现，在许多决策环境中，AI解释无法促进这种验证。此外，无论采用何种解释方法，大多数任务本质上就不允许简单验证，从而限制了任何类型解释的潜在价值。我们还比较了互补性能目标与适当依赖目标，并将后者分解为基于结果和基于策略的依赖概念。