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TRUVACE RECORD VERSION
record: TRV-2026-0344
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T08:53:05.356750Z
status: published
lens: trace
sector: business
headline: The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence
dek: The risks posed by artificial intelligence (AI) concern academics, auditors, policymakers, AI companies, and the public. Researchers, policymakers, and technology companies discuss AI risks using inconsistent terminology-the same word may describe different problems, while different words describe identical concerns. This fragmentation impedes coordinated responses to AI challenges. We address this by creating the AI Risk Repository: a living database of 1,725 risks extracted from 74 existing taxonomies and fram…
gain_title: Creation of a living database of 1,725 risks from 74 taxonomies provides a shared reference that enables more coordinated auditing and governing of AI systems.
problem_title: AI systems pose risks across seven effect areas ranging from discrimination and privacy violations to misinformation and weapons development that concern auditors, policymakers, companies and the public.
trace_subject: AI system risks and governance
gain_reading: Creation of a living database of 1,725 risks from 74 taxonomies provides a shared reference that enables more coordinated auditing and governing of AI systems.
gain_evidence: a living database of 1,725 risks extracted from 74 existing taxonomies and frameworks | This shared reference enables more coordinated approaches to discussing, researching, auditing, and governing AI systems across sectors and jurisdictions
problem_reading: AI systems pose risks across seven effect areas ranging from discrimination and privacy violations to misinformation and weapons development that concern auditors, policymakers, companies and the public.
problem_evidence: The risks posed by artificial intelligence (AI) concern academics, auditors, policymakers, AI companies, and the public | from discrimination and privacy violations to misinformation and weapons development
quick_read: On March 30 2026, a peer-reviewed paper in Patterns described the AI Risk Repository, a living database of 1,725 risks extracted from 74 existing taxonomies and frameworks. The authors created two complementary systems to organize them: a Causal Taxonomy by origin, intent and timing, and a Domain Taxonomy by effects across seven areas.

The work matters because fragmented language around AI risks makes auditing and governing difficult for academics, companies and policymakers. The repository offers a common reference to coordinate research and oversight, but the source does not report measured reductions in harm or adoption outcomes, only the structure and intent of the database.
limitation: 
tag: Automated dual reading
key_points: Authors extracted 1,725 risks from 74 existing taxonomies and frameworks to build the AI Risk Repository as a living database. | Causal Taxonomy classifies risks by origin: which entity causes them (human or AI), whether intentional, and timing before or after deployment. | Domain Taxonomy classifies risks by effects across seven areas including discrimination, privacy violations, misinformation and weapons development. | Fragmented terminology where same word describes different problems impedes coordinated responses, motivating the shared reference.
rundown: By March 30 2026, the authors had compiled 1,725 risks from 74 taxonomies into a living database, noting that inconsistent terminology impedes coordinated responses.

They structured the repository with a Causal Taxonomy focused on entity, intent and timing, and a Domain Taxonomy focused on effects, intended for use in discussing, researching, auditing and governing across sectors and jurisdictions.
sources:
- peer_reviewed | Patterns | https://doi.org/10.1016/j.patter.2026.101517 | 2026-03-30
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