对话式可解释AI提升理解与信任，但可能加剧过度依赖。
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant
- 用大模型驱动的对话界面增强XAI解释能力
- 用户对AI理解更深、信任更高，但普遍过度依赖
- 适合关注人机协作与AI可信度的研究者
可解释人工智能（XAI）方法旨在帮助用户理解AI系统如何做出特定预测。受对话式用户界面研究启发，我们提出将对话界面与现有XAI方法结合，以提升用户参与度和对AI系统的理解。本文探讨了对话式XAI界面在用户理解、信任及依赖程度上的影响。相比传统XAI仪表板，对话式界面显著提升了用户的理解水平和信任感。然而，两类界面的用户均表现出明显的过度依赖现象；由大语言模型（LLM）驱动的增强对话进一步放大了这种依赖。我们认为，其潜在原因在于两种界面共同引发的‘解释深度错觉’。研究结果对设计有效的人机协同对话式XAI系统具有重要启示。
原文摘要 · Abstract (English)
Explainable artificial intelligence (XAI) methods are being proposed to help interpret and understand how AI systems reach specific predictions. Inspired by prior work on conversational user interfaces, we argue that augmenting existing XAI methods with conversational user interfaces can increase user engagement and boost user understanding of the AI system. In this paper, we explored the impact of a conversational XAI interface on users' understanding of the AI system, their trust, and reliance on the AI system. In comparison to an XAI dashboard, we found that the conversational XAI interface can bring about a better understanding of the AI system among users and higher user trust. However, users of both the XAI dashboard and conversational XAI interfaces showed clear overreliance on the AI system. Enhanced conversations powered by large language model (LLM) agents amplified over-reliance. Based on our findings, we reason that the potential cause of such overreliance is the illusion of explanatory depth that is concomitant with both XAI interfaces. Our findings have important implications for designing effective conversational XAI interfaces to facilitate appropriate reliance and improve human-AI collaboration. Code can be found at https://github.com/delftcrowd/IUI2025_ConvXAI
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