用证据密度可视化破解AI内容信任危机
Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty
- 通过证据密度图展示文本中可验证主张的分布，替代简单的AI生成标签
- 用户研究显示，该界面使真假内容辨别力提升4.15分（效应量1.82）
- 动态查询下一致性否决机制最具判别力，适合内容审核与可信度评估场景
随着生成式AI让精美文本低成本产出，用户无法再以流畅度判断真实性。我们称之为‘流畅性陷阱’：用户既信任流畅的幻觉内容，又在披露为AI生成后贬低准确信息。二元的‘由AI制作’标签仅提供作者身份披露，但未呈现支持主张的证据。本文提出‘来源密度’（Provenance Density）——一种证据可视化界面，展示文本中经验证主张的密集程度。81名参与者的用户研究表明，理想化的来源密度界面使真实与虚构内容的辨识度差距达+4.15分（效应量d=1.82），而无信号组则无显著区分能力。对200个样本的技术审计发现，仅靠检索密度不足；出人意料的是，‘一致性否决’在动态查询中承载了主要判别信号。当AI生成内容与人类写作难以区分时，有效透明度必须从作者披露转向证据可视化。
原文摘要 · Abstract (English)
As generative AI makes polished prose cheap to produce, users can no longer rely on fluency as a proxy for truth. We call this failure mode the Fluency Trap: users trust fluent hallucinations while also discounting accurate content once it is disclosed as AI-generated. Binary ``Made with AI'' labels respond with authorship disclosure, but they do not show what supports a claim. We propose Provenance Density, an evidence-visualization interface that shows the density of verified claims in a text. In a user study with 81 participants, an idealized Provenance Density interface produced a large discernment gap between truth and fabrication ($+4.15$ points, $d=1.82$), whereas participants given no signal showed no detectable discrimination. A technical audit with 200 samples shows that retrieval density alone is insufficient; unexpectedly, the Consistency Veto carries most of the discriminative signal on dynamic queries. As AI-generated content becomes indistinguishable from human writing, effective transparency must move from authorship disclosure toward evidence visualization.
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