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

• URL: http://arxiv.org/abs/2609.03460v1
• Date: Thu, 03 Sep 2026 07:18:42 GMT
• Title: Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty
• Abstract summary: generative AI makes polished prose cheap to produce, users can no longer rely on fluency as a proxy for truth.<n>We call this failure mode the Fluency Trap: users trust fluent hallucinations while also discounting accurate content once it is disclosed as AI-generated.<n>We propose Provenance Density, an evidence-visualization interface that shows the density of verified claims in a text.
• Score: 28.22285512950749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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