解释是否会增加人工智能人群生成的假新闻警告的有效性？
Do explanations increase the effectiveness of AI-crowd generated fake news warnings?
摘要：
社交媒体平台越来越多地部署复杂的干预措施，以帮助用户发现虚假新闻。使用将众包与人工智能（AI）相结合的技术来标记虚假新闻，提供了一种很有前途的方法，可以在不审查内容的情况下告知用户潜在的低质量信息，但用户也很难理解。在这项研究中，我们研究了用户如何在他们的共享意图，他们提供了一个假设的人类-人工智能混合系统的信息作出反应。我们问i）这些警告是否会增加社交媒体分享意图的识别，ii）解释标签系统如何工作是否可以提高警告的有效性。为此，我们进行了一项研究（$N=1473$美国人），参与者表示他们分享内容的可能性。参与者被随机分配到一个对照组，一个贴上虚假内容标签的治疗组，或者一个贴上警告标签并解释它们是如何产生的治疗组。我们发现了明确的证据表明，这两种治疗方法都增加了共享识别力，并且有方向性的证据表明，解释增加了警告的有效性。有趣的是，我们并没有发现解释增加了自我报告的对警告标签的信任，尽管我们确实发现了一些证据表明参与者发现带有解释的警告信息更丰富。总之，这些结果对设计和部署透明的错误信息警告标签以及更广泛的人工智能介导系统具有重要意义。
英文摘要：
Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study ($N=1473$ Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
