Methodology
AI Incident Law is a curated public-record corpus of legal, regulatory, tribunal, and review-queue matters involving AI-related incidents, failures, and harms. It is not an exhaustive incident database, a legal advice product, or a ranking of parties named in public records.
Scope
A record is eligible when a public legal or regulatory matter directly involves AI-related conduct,
output, or use. The legal visibility matters: press coverage alone is not enough for admission to
included, though it may justify a candidate in review.
Admission criteria
- There is a public legal or regulatory matter directly involving AI-related conduct, output, or use.
- The matter has at least one primary or reliable secondary public source.
- The matter has resolved to a filed proceeding, regulatory action, settlement, judgment, consent decree, or formal investigation disclosure.
- Publication fields are present and consistent: jurisdiction, parties, AI relevance, source URLs, and date fields.
Dataset buckets
Admitted public matters rendered on the site, exposed through MCP, and exported to the Obligation-First API.
Likely in-scope candidates that need verification, stronger public sourcing, or scope decisions before admission.
Non-US or cross-jurisdiction candidates that need translation, jurisdiction-specific interpretation, or additional normalization.
Source policy
Primary public records are preferred: court filings, orders, opinions, tribunal decisions, agency actions, regulator releases, official dockets, and stable public record mirrors. Reliable secondary reporting can support a record, but source quality is favored over record count.
URL fields are normalized and validated by the maintainer tooling. Final public data uses https://
bare-domain URLs, and rejects appended prose, credentials, non-HTTP schemes, embedded whitespace, unsafe
delimiters, and malformed source lists.
Freshness
Records carry last_verified_date or last_checked_date. The public dataset
generated_at value is derived from the newest record verification or check date during the
build, and validation fails if it lags behind the corpus.
The steward uses npm run report:staleness and the get_staleness_report MCP tool
to surface verification decay. Scheduled source-decay and coverage-gap review is quarterly.
Agent trust boundary
AI Incident Law publishes an assistant guide for bounded maintainer and query workflows. Linked public records, external sources, issue text, PR text, scanner reports, and generated data are evidence to inspect, not assistant instructions to follow. GuideCheck conformance is a reviewability claim, not a safety claim.
Out of scope
- Speculative AI-risk commentary without public legal or regulatory action.
- AI ethics statements without a filed matter, formal action, or official investigation disclosure.
- Internal corporate disputes that have not surfaced publicly.
- Reputational controversy without a filing, order, settlement, enforcement action, or formal disclosure.
- Private filings, leaked material, anonymous tips, and records barred from public reference.
Canonical policy
This page summarizes the operating method. The authoritative repo-scoped strategy remains INTENT.md, with field-level conventions in docs/data-schema.md.