Summary of findings.

While there has been extensive research on techniques for explainable artificial intelligence (XAI) to enhance AI recommendations, the metacognitive processes in interacting with AI explanations remain underexplored. This study examines how AI explanations impact human decision-making by leveraging cognitive mechanisms that evaluate the accuracy of AI recommendations. We conducted a large-scale experiment (N = 4,302) on Amazon Mechanical Turk (AMT), where participants classified radiology reports as normal or abnormal. Participants were randomly assigned to three groups: a) no AI input (control group), b) AI prediction only, and c) AI prediction with explanation. Our results indicate that AI explanations enhanced task performance. Our results indicate that explanations are more effective when AI prediction confidence is high or users’ self-confidence is low. We conclude by discussing the implications of our findings.