Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks

TL;DR

This paper develops a multidimensional framework and pathway models to systematically analyze and mitigate catastrophic AI risks across various hazard types, enhancing understanding and intervention strategies.

Contribution

It introduces a comprehensive dimensional characterization and causal pathway modeling for six major AI catastrophic risks, providing a structured approach for risk management.

Findings

Dimensional analysis clarifies risk attributes across seven key factors.

Pathway models identify step-by-step progression from hazards to harms.

Framework supports targeted mitigation strategies.

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.