[Submitted on 3 Sep 2026]
Title:Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty
View a PDF of the paper titled Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty, by Qing Zhang and 4 other authors
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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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.03460 [cs.AI]
(or arXiv:2609.03460v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.03460
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Qing Zhang [view email] [v1] Thu, 3 Sep 2026 07:18:42 UTC (98 KB)
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