AI Control: Improving Safety Despite Intentional Subversion

Summary

This post introduces the 'AI Control' research agenda, which focuses on ensuring AI systems remain safe and beneficial even if they are deceptively misaligned or actively trying to subvert oversight. It proposes practical protocols and evaluation frameworks for maintaining human control over AI systems that may be adversarially behaving, complementing alignment approaches by treating safety as robust to worst-case AI behavior.

Key Points

• •AI Control focuses on keeping AI systems safe even when assuming the AI may be actively trying to undermine human oversight or cause harm.
• •The agenda is complementary to alignment research: alignment tries to make AIs want good outcomes, control tries to ensure safety regardless of AI intentions.
• •Key mechanisms include human oversight protocols, sandbagging evaluations, and red-teaming AI systems to identify subversion strategies.
• •The framework emphasizes evaluating control measures by assuming a worst-case adversarial AI, providing stronger safety guarantees than alignment alone.
• •Practical deployment strategies are proposed for high-stakes AI tasks where misaligned behavior would be catastrophic.