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 contexts fundamentally do not allow verification, regardless of explanation method. We conclude with a discussion of potential approaches for more effective explainable AI-advised decision making and human-AI collaboration.

翻译：当前关于人工智能辅助决策的文献（涉及可解释AI系统协助人类决策者）呈现出一系列不确定且令人困惑的结果。为综合这些发现，我们提出一个简洁的理论，阐明AI解释为何频繁未能引发适度依赖并实现互补性决策表现。我们认为，解释的有用性仅在于其能使人类决策者验证AI预测的正确性——这与可解释性、阐明AI推理过程等其他诉求形成对比。先前研究发现，在许多决策情境中，AI解释无法实现此类验证。更关键的是，无论采用何种解释方法，大多数情境从根本上就不允许这种验证。最后，我们讨论了如何通过潜在方法实现更有效的可解释AI辅助决策及人机协作。