In search of verifiability: Explanations rarely enable complementary performance in AI-advised decision making

Abstract

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. In contrast to other common desiderata, for example, interpretability or spelling out the AI's reasoning process, we argue that explanations are only useful to the extent that they allow a human decision maker to verify the correctness of the AI's prediction. Prior studies find in many decision making contexts that 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.