[论文解读] The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence

Peter Slattery, Alexander K. Saeri+8 more|arXiv (Cornell University)|Aug 14, 2024

Artificial Intelligence in Healthcare and Education被引用 24

一句话总结

本论文将74个AI风险框架编成并整合成统一的分类体系和包含1,725项风险的数据库，强调人类决策与AI系统各自贡献显著的风险（分别为38%和42%）。

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.

研究动机与目标

• 通过系统分析迄今发布的主要AI风险框架，提供对AI风险的全面编目。
• 将多样化的风险分类汇聚成一个单一的连贯系统，以便跨研究比较和政策映射。
• 量化AI风险的来源，揭示模式以指导更安全的AI开发和监管。

提出的方法

• 系统性分析迄今发布的74个AI风险框架。
• 提取并协调跨框架的1,725个不同风险。
• 构建两套分类系统，以揭示AI风险来源中的模式。
• 量化人类决策对总体风险的贡献与AI系统的贡献。

实验结果

研究问题

• RQ1 现有AI风险框架的范围和内容是什么（74个框架，1,725项风险），它们如何统一？
• RQ2 如何构建一个全面、共享的AI风险分类法和数据库，以便更好地进行比较和政策使用？
• RQ3 人类决策与AI系统对AI相关风险的相对贡献是什么（例如原因之间的分布）？

主要发现

• 在74个AI风险框架中识别出1,725项不同的风险。
• 人类决策占AI风险的38%，而AI系统占42%。
• 统一的风险存储库使研究人员、政策制定者和审计人员之间的风险管理更加协调和全面。
• 该工作为AI安全中的风险评估、监管和系统评估提供了实用工具。