🤖 AI Summary
Current AI risk research lacks a systematic framework and rigorous causal modeling of pathways from hazardous AI capabilities to real-world harms. This paper addresses six categories of catastrophic AI risks by proposing the first seven-dimensional risk characterization framework—spanning intent, capability, agent type, and other critical dimensions—and introducing a stepwise causal pathway model (“Hazard → Harm → Consequence”). Methodologically, it integrates multidimensional feature analysis with formal causal path modeling to enable computationally tractable representation of risk evolution. The contributions are threefold: (1) a scalable, structured analytical framework for AI catastrophe risk assessment; (2) a decision-support tool that jointly enables general-purpose mitigation strategies and scenario-specific interventions; and (3) a theoretical and operational foundation for end-to-end AI risk governance across the full value chain.
📝 Abstract
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.