Harnessing Explanations to Bridge AI and Humans

Explanations alone don't guarantee better decisions: Despite the widespread development of interpretable AI methods designed to explain machine predictions, recent studies show that simply providing explanations does not automatically lead to improved human decision-making, challenging a core assumption in the field.

Critical applications need human oversight: In high-stakes domains like recidivism prediction and medical diagnosis, full automation is often inappropriate due to ethical and legal concerns, making the human-AI partnership essential rather than optional.

Gap between explanation quality and human performance: There is a significant disconnect between how well AI systems can explain their predictions and whether those explanations actually help humans make better decisions, indicating that technical explainability alone is insufficient.

Rethinking explanation design is necessary: The paper calls for future research directions focused on closing the gap between explanation efficacy and human performance improvement, suggesting that how explanations are presented and communicated to humans needs fundamental reconsideration.

Human-centered approach required: Moving forward, responsible AI development in critical applications must prioritize understanding how humans actually use and benefit from explanations, rather than assuming that more interpretable models automatically lead to better outcomes.