The Role of Risk Modeling in Advanced AI Risk Management

2025-12-09

AI Summary

Contemporary AI systems face novel, uncertain, and potentially catastrophic risks, yet existing risk management frameworks lack infrastructure grounded in rigorous causal modeling. Method: This study introduces the first risk modeling paradigm integrating causal scenario construction with quantitative risk assessment, systematically unifying fault trees, event trees, FMEA/FMECA, STPA, and Bayesian networks—adapted specifically to AI system characteristics. Contribution/Results: (1) It establishes a dual-track governance framework combining deterministic assurance with probabilistic risk evaluation; (2) it defines verifiable safety-architecture requirements; and (3) it develops a regulator-oriented, iterative modeling process that anchors standardized risk assessment to societal risk tolerance thresholds. The resulting methodology provides a scalable, principled blueprint for AI risk management and actionable governance pathways.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks. Managing these risks requires a mature risk-management infrastructure whose cornerstone is rigorous risk modeling. We conceptualize AI risk modeling as the tight integration of (i) scenario building$-$causal mapping from hazards to harms$-$and (ii) risk estimation$-$quantifying the likelihood and severity of each pathway. We review classical techniques such as Fault and Event Tree Analyses, FMEA/FMECA, STPA and Bayesian networks, and show how they can be adapted to advanced AI. A survey of emerging academic and industry efforts reveals fragmentation: capability benchmarks, safety cases, and partial quantitative studies are valuable but insufficient when divorced from comprehensive causal scenarios. Comparing the nuclear, aviation, cybersecurity, financial, and submarine domains, we observe that every sector combines deterministic guarantees for unacceptable events with probabilistic assessments of the broader risk landscape. We argue that advanced-AI governance should adopt a similar dual approach and that verifiable, provably-safe AI architectures are urgently needed to supply deterministic evidence where current models are the result of opaque end-to-end optimization procedures rather than specified by hand. In one potential governance-ready framework, developers conduct iterative risk modeling and regulators compare the results with predefined societal risk tolerance thresholds. The paper provides both a methodological blueprint and opens a discussion on the best way to embed sound risk modeling at the heart of advanced-AI risk management.

Problem

• Develop risk modeling for advanced AI systems
• Integrate scenario building with risk estimation
• Establish governance with deterministic and probabilistic safety

Innovation

• Integrates scenario building with risk estimation
• Adapts classical risk modeling techniques to AI
• Proposes verifiable safe AI architectures for governance