Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks (2206.08966v3)

Abstract: AI systems can provide many beneficial capabilities but also risks of adverse events. Some AI systems could present risks of events with very high or catastrophic consequences at societal scale. The US National Institute of Standards and Technology (NIST) has been developing the NIST Artificial Intelligence Risk Management Framework (AI RMF) as voluntary guidance on AI risk assessment and management for AI developers and others. For addressing risks of events with catastrophic consequences, NIST indicated a need to translate from high level principles to actionable risk management guidance. In this document, we provide detailed actionable-guidance recommendations focused on identifying and managing risks of events with very high or catastrophic consequences, intended as a risk management practices resource for NIST for AI RMF version 1.0 (released in January 2023), or for AI RMF users, or for other AI risk management guidance and standards as appropriate. We also provide our methodology for our recommendations. We provide actionable-guidance recommendations for AI RMF 1.0 on: identifying risks from potential unintended uses and misuses of AI systems; including catastrophic-risk factors within the scope of risk assessments and impact assessments; identifying and mitigating human rights harms; and reporting information on AI risk factors including catastrophic-risk factors. In addition, we provide recommendations on additional issues for a roadmap for later versions of the AI RMF or supplementary publications. These include: providing an AI RMF Profile with supplementary guidance for cutting-edge increasingly multi-purpose or general-purpose AI. We aim for this work to be a concrete risk-management practices contribution, and to stimulate constructive dialogue on how to address catastrophic risks and associated issues in AI standards.

Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks

This paper, authored by Barrett, Hendrycks, Newman, and Nonnecke, addresses the pressing need for rigorous standards and guidelines to manage the catastrophic risks associated with advanced AI systems. Focused on translating high-level risk management principles into actionable guidance, the authors contribute specific recommendations for the National Institute of Standards and Technology's (NIST) artificial intelligence Risk Management Framework (AI RMF).

Overview

AI systems, while offering transformative potential, pose risks of catastrophic events on a societal scale. Recognizing this, NIST has initiated the development of the AI RMF, providing voluntary guidelines for AI risk assessment and management. However, the current framework lacks sufficient detail concerning high-impact risks, which motivated the authors to propose actionable guidance.

Key Contributions

1. Risk Identification and Management: The paper advocates for systematic processes to foresee potential unintended uses and misuses of AI systems. It highlights the necessity of integrating human rights considerations into AI development and proposes methodologies for impact identification and assessment that account for catastrophic risk factors.
2. Scope and Impact Assessments: Focusing on early identification, the authors recommend strategies to include catastrophic-risk factors within risk and impact assessments. Emphasis is placed on evaluating long-term impacts and enhancing the risk management practices to address novel "Black Swan" events and systemic risks, which are often overlooked.
3. AI RMF 1.0 Recommendations: Detailed recommendations for NIST's AI RMF 1.0 include criteria for risk assessment during different stages of AI development, highlighting the importance of lifecycle compatibility, enterprise risk management compatibility, and integration with existing standards.
4. Future Directions: The authors suggest creating an AI RMF Profile dedicated to multi-purpose or general-purpose AI systems, such as foundation models exemplified by systems like GPT-3 or DALL-E, which pose unique risks due to their broad applicability and emergent properties.

Methodological Approach

The paper outlines procedures for drafting guidance through a proactive approach, focusing on tractable components with immediate relevance. These recommendations are framed to be easily incorporated into existing ERM processes and are designed to align with other international standards (e.g., ISO/IEC, IEEE).

Implications and Future Directions

The authors stress the importance of addressing the cascading failures of advanced AI systems, which could occur across multiple sectors. By aligning AI system objectives with human values and integrating rigorous risk management protocols, the paper aims to mitigate catastrophic risks while fostering innovation. The long-term aspiration is that these guidelines will inform legislative mandates and industry practices, promoting safer AI deployment.

The proposed roadmap for the AI RMF includes development of more extensive governance mechanisms, characterization of AI generality, and recursive improvement potential, all aimed at navigating the transformative impacts and curtailing the risks of emerging AI technologies.

In conclusion, this paper delivers a comprehensive guide to managing AI risks, synthesizing practical guidance with a strategic vision for future risk management frameworks. It prompts a necessary discourse on AI governance and underscores the urgency of developing robust, scalable solutions for AI risk mitigation in high-consequence scenarios.