AI安全框架的评分量规
A Grading Rubric for AI Safety Frameworks
AI总结：
针对当前AI企业AI安全框架缺乏统一评判标准的问题，本文提出含7项标准21项指标的A-F级评分量规及三种应用方法，以支撑精细化比较、改进方向识别与负责任AI开发的良性竞争。
摘要：
过去一年，人工智能（AI）公司正越来越多地采用AI安全框架。这类框架阐明了企业计划如何将前沿AI系统开发与部署相关的潜在风险控制在可接受水平。Anthropic、OpenAI和Google DeepMind等主要参与者已发布各自的框架，另有13家公司表示有意在2025年2月前发布类似框架。鉴于AI安全框架在AI企业识别和应对其系统带来的不可接受风险的工作中处于核心地位，它们理应受到严格审查。为让政府、学术界和公民社会能够对这些框架作出评判，本文提出了一套评分量规。该量规包含7项评估标准以及将标准具体化的21项指标，每项标准可按A（黄金标准）到F（不合格）的等级进行评分。本文还提出了三种应用该量规的方法：调查法、Delphi研究法和审计法。这套评分量规旨在实现不同框架间的精细化比较，识别潜在的改进方向，并推动负责任AI开发领域的良性向上竞争。
英文摘要：
Over the past year, artificial intelligence (AI) companies have been increasingly adopting AI safety frameworks. These frameworks outline how companies intend to keep the potential risks associated with developing and deploying frontier AI systems to an acceptable level. Major players like Anthropic, OpenAI, and Google DeepMind have already published their frameworks, while another 13 companies have signaled their intent to release similar frameworks by February 2025. Given their central role in AI companies' efforts to identify and address unacceptable risks from their systems, AI safety frameworks warrant significant scrutiny. To enable governments, academia, and civil society to pass judgment on these frameworks, this paper proposes a grading rubric. The rubric consists of seven evaluation criteria and 21 indicators that concretize the criteria. Each criterion can be graded on a scale from A (gold standard) to F (substandard). The paper also suggests three methods for applying the rubric: surveys, Delphi studies, and audits. The purpose of the grading rubric is to enable nuanced comparisons between frameworks, identify potential areas of improvement, and promote a race to the top in responsible AI development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
