Strengthening Resilience to AI Risk: A Guide for UK Policymakers
May 4, 2024
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A report identifies levers in different stages of AI lifecycle, risk mitigation hierarchy advocated for early prevention strategies. Policy interventions emphasized for AI development safety, including model reporting and third-party auditing. Importance of reassessing research funding in AI, setting red lines, and strategies for mitigating AI risks in UK policy like legal liability laws and investment screening discussed.
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Quick takeaways
AI risks can be categorized based on different stages of the AI lifecycle, aiding policymakers in prioritizing preventive actions.
Government plays a crucial role in promoting safe AI development through incentives, transparency, and accountability measures.
Deep dives
Identifying AI Risk Pathways
The paper focuses on categorizing AI risks based on the stages of the AI lifecycle: design, training and testing, deployment, and longer-term deployment. It highlights various risk pathways in each stage, such as data privacy, security vulnerabilities, and dangerous capabilities. By mapping out these risks systematically, policymakers can prioritize actions to prevent harms in the early stages of AI development.
Policy Interventions for Resilience
The podcast emphasizes three main categories of AI policy interventions aligned with different development stages: creating visibility and understanding, promoting best practices, and establishing incentives and enforcing regulation. It stresses the importance of enhancing transparency, collaborating on best practices, and implementing incentives to ensure the safe development and deployment of AI systems.
Government Role in AI Governance
The episode underscores the crucial role of government in fostering safe AI development by encouraging adherence to best practices and holding developers accountable. It discusses soft incentives like government-supported auditing and legal enforcement mechanisms to ensure compliance. The emphasis is on creating a robust AI assurance ecosystem and allocating public resources to enhance AI safety.
This report from the Centre for Emerging Technology and Security and the Centre for Long-Term Resilience identifies different levers as they apply to different stages of the AI life cycle. They split the AI lifecycle into three stages: design, training, and testing; deployment and usage; and longer-term deployment and diffusion. It also introduces a risk mitigation hierarchy to rank different approaches in decreasing preference, arguing that “policy interventions will be most effective if they intervene at the point in the lifecycle where risk first arises.”
While this document is designed for UK policymakers, most of its findings are broadly applicable.