ArticlePatterns (New York, N.Y.)2026
The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review.Journal of medical Internet research · 2026Pooled it
- AI-Augmented Clearinghouse to Facilitate Evidence-Based Decision-Making and Social Spending: a Conceptual Framework.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Risk and liability in the deployment of AI systems for surgery: a SAGES white paper.Surgical endoscopy · 2026Review
- The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence.Patterns (New York, N.Y.) · 2026Article
- Article
- Data concerns undermine AI applications for climate.npj climate action · 2026Article
- Evaluation framework for assessing the quality and safety of artificial intelligence solutions for the acute care surgeon: a narrative review from the Eastern Association for the Surgery of Trauma quality, safety, and outcomes committee.Trauma surgery & acute care open · 2026Review
- Artificial Intelligence in Haematologic Diagnostics: Current Applications and Future Perspectives.Acta haematologica · 2026Review
- Artificial intelligence in human immunodeficiency virus mutation prediction and drug design: Advancing personalized treatment and prevention.Pharmaceutical science advances · 2025Review
- Frontier AI developers need an internal audit function.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- The Geometry of Concepts: Sparse Autoencoder Feature Structure.Entropy (Basel, Switzerland) · 2025Article
- Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset.IEEE journal of translational engineering in health and medicine · 2025Article
- Medicine for artificial intelligence: applying a medical framework to AI anomalies.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 frameworks. We organize these risks using two complementary classification systems. The Causal Taxonomy classifies risks by their origins: which entity causes them (human or AI), whether intentional, and when they occur (before or after deployment). The Domain Taxonomy classifies risks by their effects across seven areas, from discrimination and privacy violations to misinformation and weapons development. This shared reference enables more coordinated approaches to discussing, researching, auditing, and governing AI systems across sectors and jurisdictions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.