专家调查揭示生成式AI制造假信息的系统性风险，主张建立可复现的溯源机制。
The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance
- 通过专家问卷调查，分析多模态伪造内容威胁
- 文本生成比深伪视频更易引发认知分裂和虚假共识
- 强调数据溯源与方法可复现性对信息可信度的关键作用
生成式人工智能（GenAI）已将虚假信息生产从人工制造转向自动化大规模操控。本文基于首波纵向专家感知调查（N=21），涵盖AI研究者、政策制定者与虚假信息专家，评估多模态威胁（文本、图像、音频、视频）的严重性，并检验现有缓解策略。结果显示，尽管深伪视频具有即时“冲击力”，但大规模文本生成在政治领域更可能引发“认知碎片化”与“合成共识”等系统性风险。专家对技术检测工具普遍持怀疑态度，更倾向支持溯源标准与监管框架，尽管存在实施障碍。文章指出，当前核心挑战在于测量：缺乏标准化基准与可复现性清单，使合成媒体的追踪与应对难以实现。为此，建议将信息完整性视为基础设施，强化数据溯源与方法可复现性。
原文摘要 · Abstract (English)
The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists. It examines the perceived severity of multimodal threats -- text, image, audio, and video -- and evaluates current mitigation strategies. Results indicate that while deepfake video presents immediate "shock" value, large-scale text generation poses a systemic risk of "epistemic fragmentation" and "synthetic consensus," particularly in the political domain. The survey reveals skepticism about technical detection tools, with experts favoring provenance standards and regulatory frameworks despite implementation barriers. GenAI disinformation research requires reproducible methods. The current challenge is measurement: without standardized benchmarks and reproducibility checklists, tracking or countering synthetic media remains difficult. We propose treating information integrity as an infrastructure with rigor in data provenance and methodological reproducibility.
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