The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence
Massachusetts Institute of Technology · The University of Queensland · +3 more institutions
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…
Citation impact
- FWCI
- 57.82
- Percentile
- 100%
- References
- 24
Authors
10- PSPeter SlatteryCorresponding
Massachusetts Institute of Technology
- AKAlexander K. Saeri
The University of Queensland, Massachusetts Institute of Technology
- EAEmily A. C. Grundy
The University of Queensland, Massachusetts Institute of Technology
- JGJess Graham
The University of Queensland, Massachusetts Institute of Technology
- MNMichael Noetel
The University of Queensland, Massachusetts Institute of Technology
Topics & keywords
- Taxonomy (biology)
- Computer science
- Risk assessment
- Data science
- Ecology
- Biology
- Computer security