AI Error Difficulty Modulates the Effectiveness of Explainability in Decision Support Systems

In AI decision support, explainability (e.g., disclosing system weaknesses) should help users spot erroneous recommendations, but its efficacy may depend on task difficulty. We ran three experiments in a simulated medical visual detection task. In Experiment 1, we manipulated error difficulty (easy vs. difficult) and explainability (nonXAI vs. XAI). Experiment 2 added a virtually impossible error difficulty. Across both, explainability consistently reduced reliance on incorrect recommendations for difficult errors, showed no benefit for easy errors, and a small benefit for impossible errors. Experiment 3 varied error and task difficulty within-subjects and extended these patterns; as task difficulty rose, participants behaved less rationally, exhibiting both under- and overreliance. Notably, these behavioral benefits were generally not accompanied by reduced trust in the AI system. Our findings suggest that disclosing system weaknesses enhances detection of AI errors but is most effective for tasks of moderate difficulty where AI recommendations are still verifiable.