The illusion of consensus occurs when people believe there is consensus across multiple sources, but the sources are the same and thus there is no "true" consensus. We explore this phenomenon in the context of an AI-based intelligent agent designed to augment metacognition on social media. Misinformation, especially on platforms like Twitter, is a global problem for which there is currently no good solution. As an explainable AI (XAI) system, the agent provides explanations for its decisions on the misinformed nature of social media content. In this late-breaking study, we explored the roles of trust (attitude) and reliance (behaviour) as key elements of XAI user experience (UX) and whether these influenced the illusion of consensus. Findings show no effect of trust, but an effect of reliance on consensus-based explanations. This work may guide the design of anti-misinformation systems that use XAI, especially the user-centred design of explanations.

翻译：共识错觉是指人们认为多个来源之间存在共识，但实际上这些来源是相同的，因此并不存在“真正的”共识。我们在一个旨在增强社交媒体元认知的人工智能智能代理的背景下探索了这一现象。错误信息，尤其是在Twitter等平台上，是一个全球性问题，目前尚无良好的解决方案。作为一个可解释人工智能（XAI）系统，该代理为其关于社交媒体内容虚假性质的决策提供解释。在这项最新研究中，我们探讨了信任（态度）和依赖（行为）作为XAI用户体验（UX）关键要素的作用，以及这些因素是否影响了共识错觉。研究发现，信任对基于共识的解释无显著影响，但依赖则产生了影响。这项工作可能指导使用XAI的反虚假信息系统的设计，尤其是以用户为中心的解释设计。