The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence
The risks posed by Artificial Intelligence (AI) are of considerable concern to academics, auditors, policymakers, AI companies, and the public. However, a lack of shared understanding of AI risks can impede our ability to comprehensively discuss, research, and react to them. This paper addresses thi...
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Main Authors | , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
14.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The risks posed by Artificial Intelligence (AI) are of considerable concern
to academics, auditors, policymakers, AI companies, and the public. However, a
lack of shared understanding of AI risks can impede our ability to
comprehensively discuss, research, and react to them. This paper addresses this
gap by creating an AI Risk Repository to serve as a common frame of reference.
This comprises a living database of 777 risks extracted from 43 taxonomies,
which can be filtered based on two overarching taxonomies and easily accessed,
modified, and updated via our website and online spreadsheets. We construct our
Repository with a systematic review of taxonomies and other structured
classifications of AI risk followed by an expert consultation. We develop our
taxonomies of AI risk using a best-fit framework synthesis. Our high-level
Causal Taxonomy of AI Risks classifies each risk by its causal factors (1)
Entity: Human, AI; (2) Intentionality: Intentional, Unintentional; and (3)
Timing: Pre-deployment; Post-deployment. Our mid-level Domain Taxonomy of AI
Risks classifies risks into seven AI risk domains: (1) Discrimination &
toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors &
misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental, and
(7) AI system safety, failures, & limitations. These are further divided into
23 subdomains. The AI Risk Repository is, to our knowledge, the first attempt
to rigorously curate, analyze, and extract AI risk frameworks into a publicly
accessible, comprehensive, extensible, and categorized risk database. This
creates a foundation for a more coordinated, coherent, and complete approach to
defining, auditing, and managing the risks posed by AI systems. |
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DOI: | 10.48550/arxiv.2408.12622 |