Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies
研究对比了AI客服在出错后，使用可解释性策略（如局部解释、反事实选项）与类人策略（如道歉、提问）修复用户信任的效果，发现可解释性策略更能促使用户继续使用。
Abstract As AI-based conversational agents (CAs) increasingly automate customer service, inevitable system errors pose a threat to user trust. While eXplainable AI (XAI) techniques are well-established for ex-ante trust formation, their effectiveness for ex-post trust repair remains unexplored. This research investigates whether XAI-based repair strategies (local explanations, counterfactual options) implemented directly by the CA during the interaction can effectively repair trust after errors, compared to CA-implemented human-like strategies (apologies, asking questions). Through a controlled between-subjects online experiment ( N = 261), we examined CA repair strategies following a simulated system error, measuring subjective trust and actual continuance decisions. Our findings show that both XAI-based system-like and CASA-aligned human-like strategies repair subjective trust to similar levels, yet XAI-based explanations generate significantly higher rates of actual user continuance decisions following errors. This challenges the human-like-by-default design paradigm for CAs and demonstrates XAI's viability as a post-hoc repair mechanism, extending XAI research beyond trust formation into trust repair contexts.