{"type":"ScholarlyArticle","title":"Beyond “Made with AI”: Visualizing Provenance Density to Mitigate the Transparency Penalty","authors":[{"name":"Qing Zhang","slug":"qing-zhang","given":"Qing","family":"Zhang","affiliation":""},{"name":"Yifei Huang","slug":"yifei-huang","given":"Yifei","family":"Huang","affiliation":""},{"name":"Juyoung Lee","slug":"juyoung-lee","given":"Juyoung","family":"Lee","affiliation":""},{"name":"Thad Starner","slug":"thad-starner","given":"Thad","family":"Starner","affiliation":""},{"name":"Jun Rekimoto","slug":"jun-rekimoto","given":"Jun","family":"Rekimoto","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/857","url":"https://ijcai.org/proceedings/2026/857","pdf":"https://ijcai.org/proceedings/2026/0857.pdf","markdown":"https://ijcai.org/proceedings/2026/857.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/857","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Human-Centred AI","track":"Human-Centred AI","number":857,"pages":{"first":7712,"last":7720},"keywords":["Human-Centred AI: AI Ethics, Trust, Fairnes","Human-Centred AI: Humans and AI","Human-Centred AI: Natural Language Processing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}