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.

本文聚焦 AI 辅助决策，在 AI 解释人类决策过程中频繁失败的背景下，提出了一个简单的理论，即解释只有在允许决策者验证其正确性时才有用，我们讨论了更有效的 AI 决策解释方法和人工智能 - 人类决策的协作。