---
title: "Beyond “Made with AI”: Visualizing Provenance Density to Mitigate the Transparency Penalty"
authors:
  - "Qing Zhang"
  - "Yifei Huang"
  - "Juyoung Lee"
  - "Thad Starner"
  - "Jun Rekimoto"
doi: "10.24963/ijcai.2026/857"
url: "https://ijcai.org/proceedings/2026/857"
pdf: "https://ijcai.org/proceedings/2026/0857.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7712-7720"
track: "Human-Centred AI"
keywords:
  - "Human-Centred AI: AI Ethics, Trust, Fairnes"
  - "Human-Centred AI: Humans and AI"
  - "Human-Centred AI: Natural Language Processing"
---

# Beyond “Made with AI”: Visualizing Provenance Density to Mitigate the Transparency Penalty

Qing Zhang, Yifei Huang, Juyoung Lee, Thad Starner, Jun Rekimoto

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7712-7720. DOI: [10.24963/ijcai.2026/857](https://doi.org/10.24963/ijcai.2026/857)

## 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.