AI 中文总结
该实验研究通过实验室任务发现，人们检测GPT-2生成的深度伪造新闻时更依赖ChatGPT，2025年实验中则更依赖语言专家，高质量AI检测工具可提升检测效果，凸显GAI的双重作用。
AI 中文摘要
本实验研究调查了人们在检测AI生成的假新闻（深度伪造新闻）时，对不同建议来源的依赖情况。在实验室深度伪造检测任务中，学生参与者需识别合成深度伪造新闻文章中人类撰写（非AI生成）内容的比例，并接收来自ChatGPT（GPT-4）、人类同伴或语言专家的建议。结果显示，在检测GPT-2生成的深度伪造新闻时，参与者对ChatGPT的依赖程度高于人类同伴；对语言专家的依赖程度也高于同伴，而对专家与ChatGPT的相对依赖程度在不同实验阶段存在差异，这可能反映了人们对AI检测相关信念的时间趋势。重要的是，2025年在相同实验程序下开展的额外实验中，参与者对语言专家的依赖程度高于ChatGPT。此外，表现提升反映了依赖程度与建议质量的共同作用，主要出现在参与者依赖高质量建议时。总体而言，依赖AI检测AI生成的深度伪造可改善检测结果，但前提是基于AI的检测工具质量足够高。这些发现凸显了通用人工智能（GAI）作为深度伪造来源和缓解相关风险工具的双重作用。
英文摘要
This experimental study investigates how people rely on different sources of advice when detecting AI-generated fake news (deepfake news). In a laboratory deepfake detection task, student participants identified the proportion of human-written (non-AI-generated) content in synthetic deepfake news articles and received advice from ChatGPT (GPT-4), human peers, or linguistic experts. The results show that participants rely more on ChatGPT than on human peers when detecting GPT-2-generated deepfake news. Participants also rely more on linguistic experts than on peers, while the relative reliance on experts versus ChatGPT is mixed across experimental waves, potentially reflecting time trends in beliefs about AI-based detection. Importantly, in the additional experiment conducted in 2025 under the same experimental procedure, participants relied more on linguistic experts than on ChatGPT. Moreover, performance improvements reflect the joint role of reliance and advice quality, arising primarily when participants rely on high-quality advice. Overall, relying on AI to detect AI-generated deepfakes can improve detection outcomes, but only when AI-based detection tools are of sufficiently high quality. These findings highlight the dual role of GAI as both a source of deepfakes and a tool for mitigating related risks.
