A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management

• URL: http://arxiv.org/abs/2502.06656v3
• Date: Wed, 19 Feb 2025 16:05:47 GMT
• Title: A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management
• Authors: Simeon Campos, Henry Papadatos, Fabien Roger, Chloé Touzet, Otter Quarks, Malcolm Murray,
• Abstract summary: The recent development of powerful AI systems has highlighted the need for robust risk management frameworks. This paper presents a comprehensive risk management framework for the development of frontier AI.
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• Abstract: The recent development of powerful AI systems has highlighted the need for robust risk management frameworks in the AI industry. Although companies have begun to implement safety frameworks, current approaches often lack the systematic rigor found in other high-risk industries. This paper presents a comprehensive risk management framework for the development of frontier AI that bridges this gap by integrating established risk management principles with emerging AI-specific practices. The framework consists of four key components: (1) risk identification (through literature review, open-ended red-teaming, and risk modeling), (2) risk analysis and evaluation using quantitative metrics and clearly defined thresholds, (3) risk treatment through mitigation measures such as containment, deployment controls, and assurance processes, and (4) risk governance establishing clear organizational structures and accountability. Drawing from best practices in mature industries such as aviation or nuclear power, while accounting for AI's unique challenges, this framework provides AI developers with actionable guidelines for implementing robust risk management. The paper details how each component should be implemented throughout the life-cycle of the AI system - from planning through deployment - and emphasizes the importance and feasibility of conducting risk management work prior to the final training run to minimize the burden associated with it.

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