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Explainable Artificial Intelligence (XAI) can enhance trust in AI by offering cues that support human reasoning of AI behavior. Yet its effects on trust evolution remain unclear, especially when AI makes errors. This study examines how explanations of AI predictions influence human trust in AI-assisted decision-making under varying error severities. We tested two XAI visualizations, two AI error types, and three explanation strategies in simulated baggage screening tasks through an online study. Responses from 280 participants show that XAI representation significantly affects human compliance with AI during errors, while AI error type further shapes compliance after AI errors. AI Error type also impacts verification behaviors during AI errors, such as requesting explanations or ground truth. Moreover, strategies for conveying XAI influence perceived trust in AI, highlighting important implications for generalizing XAI effects beyond lab-based trust research.
Wang et al. (Thu,) studied this question.