构建七维框架与风险路径模型，系统分析六大AI灾难风险
Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks
- 从意图、能力、实体等七维度刻画风险特征
- 建立从隐患到危害的逐步演进路径，明确关键节点
- 为不同场景提供可操作的风险应对方案，适合政策制定者
尽管关于人工智能（AI）风险的讨论日益增多，但往往缺乏全面的多维框架和从危害到损害的具体因果路径。本文旨在弥补这一空白，研究六类常见的人工智能灾难性风险：核生化放射（CBRN）、网络攻击、突发失控、渐进失控、环境风险及地缘政治风险。首先，我们基于意图、能力、主体、极性、线性、影响范围和顺序七个关键维度对这些风险进行表征；其次，通过风险路径建模，逐步映射从初始隐患到最终损害的演进过程。该多维方法有助于系统识别风险并制定通用缓解策略，而风险路径模型则能定位特定场景下的干预点。二者结合，为全价值链的灾难性AI风险管理提供了更结构化且可操作的基础。
原文摘要 · Abstract (English)
Although discourse around the risks of Artificial Intelligence (AI) has grown, it often lacks a comprehensive, multidimensional framework, and concrete causal pathways mapping hazard to harm. This paper aims to bridge this gap by examining six commonly discussed AI catastrophic risks: CBRN, cyber offense, sudden loss of control, gradual loss of control, environmental risk, and geopolitical risk. First, we characterize these risks across seven key dimensions, namely intent, competency, entity, polarity, linearity, reach, and order. Next, we conduct risk pathway modeling by mapping step-by-step progressions from the initial hazard to the resulting harms. The dimensional approach supports systematic risk identification and generalizable mitigation strategies, while risk pathway models help identify scenario-specific interventions. Together, these methods offer a more structured and actionable foundation for managing catastrophic AI risks across the value chain.
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