The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence

2024-08-14

AGI - Artificial General Intelligence - Robotics - Safety & Alignment

AI Summary

The absence of a consensus definition and classification framework for AI risks impedes interdisciplinary research, auditing practices, and policy coordination. Method: We construct the first publicly accessible, scalable, and structured AI risk knowledge base, systematically integrating 777 distinct risks from 43 existing taxonomies. We propose a novel dual-layer classification framework: a “causal” layer (capturing entity-, intent-, and time-related dimensions) and a “domain” layer (comprising seven high-level categories and 23 subcategories), enabling standardized cross-taxonomy mapping and dynamic evolution. Our methodology combines systematic literature review, Delphi expert consultation, and optimal matching synthesis, implemented via an online collaborative platform supporting multidimensional querying and continuous curation. Contribution/Results: This living knowledge base significantly improves consistency in risk identification and enhances coordinated response across stakeholders, establishing foundational infrastructure for AI governance, auditing, and interdisciplinary research.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Lack of shared understanding of AI risks
• Need for a comprehensive AI risk database
• Classification of AI risks by causal factors and domains

Innovation

• Living database of 777 AI risks from 43 taxonomies
• Two-tier taxonomy system for AI risk classification
• Publicly accessible and extensible AI risk repository