In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making
This work addresses the challenge for researchers and practitioners in explainable AI by highlighting fundamental limitations in using explanations to achieve complementary performance, making it an incremental contribution that synthesizes existing findings.
The paper tackles the problem of AI explanations failing to improve human-AI decision-making performance, arguing that explanations only help if they enable verification of AI predictions, which is often not possible in many tasks.
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.