5 citations · 14 across the 7 of their papers we have counts for
5 papers · 1 filter
Risk Reporting for Developers' Internal AI Model Use
Oscar Delaney, Sambhav Maheshwari, Joe O'Brien +2
Frontier AI companies first deploy their most advanced models internally, for weeks or months of safety testing, evaluation, and iteration, before a possible public release. For ex…
Expert Survey: AI Reliability & Security Research Priorities
Joe O'Brien, Jeremy Dolan, Jay Kim +5
Our survey of 53 specialists across 105 AI reliability and security research areas identifies the most promising research prospects to guide strategic AI R&D investment. As compani…
Adapting cybersecurity frameworks to manage frontier AI risks: A defense-in-depth approach
Shaun Ee, Joe O'Brien, Zoe Williams +3
The complex and evolving threat landscape of frontier AI development requires a multi-layered approach to risk management ("defense-in-depth"). By reviewing cybersecurity and AI fr…
Coordinated Disclosure of Dual-Use Capabilities: An Early Warning System for Advanced AI
Joe O'Brien, Shaun Ee, Jam Kraprayoon +3
Advanced AI systems may be developed which exhibit capabilities that present significant risks to public safety or security. They may also exhibit capabilities that may be applied…
Deployment Corrections: An incident response framework for frontier AI models
Joe O'Brien, Shaun Ee, Zoe Williams
A comprehensive approach to addressing catastrophic risks from AI models should cover the full model lifecycle. This paper explores contingency plans for cases where pre-deployment…