Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty

论文概要

研究领域: AI/ML
作者: Qing Zhang, Yifei Huang, Juyoung Lee
发布时间: 2026-09-06
arXiv: 2509.00010

中文摘要

随着生成式AI使精美散文的产出成本降低，用户不能再依赖流畅性作为真实性的代理。我们将这种失败模式称为'流畅性陷阱'：用户信任流畅的幻觉，同时一旦准确内容被披露为AI生成也会对其打折扣。二元的'Made with AI'标签以作者身份披露作为回应，但它们不展示支持声明的内容。我们提出了Provenance Density（来源密度），一种证据可视化界面，显示文本中已验证声明的密度。在一项81名参与者的用户研究中，理想化的Provenance Density界面在真实与虚构之间产生了很大的辨别差距（+4.15分，d=1.82），而没有信号显示的参与者则没有可检测的辨别能力。对200个样本的技术审计显示，仅靠检索密度是不够的；出乎意料的是，Consistency Veto在动态查询上携带了大部分的辨别信号。随着AI生成内容变得与人类写作难以区分，有效的透明度必须从作者身份披露转向证据可视化。

原文摘要

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 dens...

自动采集于 2026-09-06

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