Trust and Reliance in Consensus-Based Explanations from an Anti-Misinformation Agent
Tokyo Institute of Technology（东京工业大学）；Tokyo University of Science（东京理科大学）
Takane Ueno, Yeongdae Kim, Hiroki Oura, Katie Seaborn
备注：
7 pages
发表信息：
In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA '23). Association for Computing Machinery, New York, NY, USA, Article 296, 1-7
研究在反虚假信息 AI 智能体的基于共识解释中考察信任与依赖，发现依赖而非信任会影响用户的共识错觉，为以用户为中心的 XAI 解释设计提供依据。
摘要：
共识错觉是指人们认为多个信息源之间存在共识，但这些信息源实际上相同，因此并不存在“真正的”共识。我们在一个旨在增强社交媒体上元认知的基于 AI 的智能体背景下探究这一现象。虚假信息，尤其是在 Twitter 等平台上的虚假信息，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 AI（XAI）系统，该智能体会针对其关于社交媒体内容虚假信息性质的决策提供解释。在这项最新研究中，我们探究了信任（态度）和依赖（行为）作为 XAI 用户体验（UX）关键要素的作用，以及它们是否影响共识错觉。研究结果表明，信任没有影响，但依赖对基于共识的解释存在影响。本工作或可指导使用 XAI 的反虚假信息系统设计，尤其是以用户为中心的解释设计。
英文摘要：
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.
FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China（中国科学院计算技术研究所）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；University of Science and Technology Beijing（北京科技大学）；The University of Queensland, Brisbane, Australia（昆士兰大学）
Agentic AI Enhances Physician Trust in Clinical Decision Making
Department of Computer Science and Engineering, Lehigh University（里海大学计算机科学与工程系）；Department of Epidemiology, Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院流行病学系）；Department of Medicine, Division of Cardiology, University of Florida（佛罗里达大学医学院心脏病学系）；McGovern Medical School, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦戈文医学院）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院放射学系）；McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物医学信息学院）；Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida（佛罗里达大学医学院健康结果与生物医学信息学系）
