Faster but Not Smarter? Temporal Constraints and User Compliance with AI Explanations

Le résumé fourni par la source

As AI systems become increasingly integrated into high-stakes domains such as healthcare, finance, and transportation, fostering appropriate human–AI collaboration through explainable AI (XAI) is essential. While prior research has examined interpretability and effectiveness of XAI methods, the role of temporal constraints in shaping human–AI decision behavior remains underexplored. We investigated how experimentally imposed time constraints influence decision-making and compliance with AI recommendations. In an online study with 239 participants, we used Where’s Wally? puzzles as a proxy for complex visual search tasks. Participants judged whether Wally was present while receiving AI suggestions. We manipulated task difficulty (easy vs. difficult), time constraints (with vs. without a time limit), and XAI support (baseline, AI confidence, LIME, AIConfLIME). Results show that time constraints substantially increased compliance with AI recommendations without improving accuracy. LIME-style overlays mainly affected response time rather than accuracy, suggesting explanations may function as behavioral cues under temporal limitations.

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