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<p>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 (<i>N </i>= 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.</p>

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Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Björn Konopka,
  • Manuel Wiesche

摘要

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.