Open Problems in AI Risk Modeling: Insights from a Workshop on the Technical Foundations of AI Risk Modeling
TL;DR
This work investigates the design of robust risk models to assess societal risks posed by advanced AI systems, an emerging area in AI governance, and identifies a structured agenda of open questions concerning model structure, scope, evidence integration, validation, and governance.
Abstract
We investigate the design of robust risk models to assess societal risks posed by advanced AI systems, an emerging area in AI governance. Many regulatory proposals increasingly require systemic risk assessment, but in the absence of rigorous quantitative methods, the question remains what state of the art risk modeling should look like in practice. We identify the key methodological and institutional challenges that currently limit the adoption of risk modeling. We review five research traditions that inform this problem: probabilistic risk assessment, catastrophic AI risk analysis, cybersecurity risk quantification, Bayesian causal inference, and threshold-based governance. We compare two leading proposals, scenario-based risk estimation and Bayesian network-based threshold setting. Drawing on a workshop with 22 experts and subsequent analysis, we identify a structured agenda of open questions concerning model structure, scope, evidence integration, validation, and governance. We close by outlining priorities for progress, arguing that it will depend on integrating quantitative modeling with independent evaluation, transparent and tiered disclosure, and institutions capable of maintaining and updating risk models over time.