系统梳理通用AI的潜在风险与应对措施，助力安全标准制定。
Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
- 从模型研发到部署全链路识别技术、操作与社会风险
- 汇总现有及实验性风险管控方法，覆盖多阶段风险场景
- 首份中立、自包含的风险与管理措施目录，适合政策制定者使用
新兴人工智能（AI）的发展迫切需要识别短期与长期风险，并明确相应的风险管理措施。为此，本文系统整理了通用型人工智能（GPAI）系统在模型开发、训练与部署各阶段面临的技术、操作与社会风险，同时调研了已有的和实验性的风险缓解方法。本工作是目前同类研究中首次提供详尽、描述性强、自成体系且不偏向任何监管框架的GPAI风险源与管理措施文档。其目标是帮助AI提供商、标准制定专家、研究人员、政策制定者与监管机构识别并缓解系统性风险。为便于治理与标准制定者直接使用，该目录以公共领域许可发布。
原文摘要 · Abstract (English)
There is an urgent need to identify both short and long-term risks from newly emerging types of Artificial Intelligence (AI), as well as available risk management measures. In response, and to support global efforts in regulating AI and writing safety standards, we compile an extensive catalog of risk sources and risk management measures for general-purpose AI (GPAI) systems, complete with descriptions and supporting examples where relevant. This work involves identifying technical, operational, and societal risks across model development, training, and deployment stages, as well as surveying established and experimental methods for managing these risks. To the best of our knowledge, this paper is the first of its kind to provide extensive documentation of both GPAI risk sources and risk management measures that are descriptive, self-contained and neutral with respect to any existing regulatory framework. This work intends to help AI providers, standards experts, researchers, policymakers, and regulators in identifying and mitigating systemic risks from GPAI systems. For this reason, the catalog is released under a public domain license for ease of direct use by stakeholders in AI governance and standards.
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