AI Control

AI control research focuses on ensuring the safe and reliable operation of increasingly autonomous artificial intelligence systems. Current efforts concentrate on developing robust control protocols, often modeled as games between a system designer and a potential adversary, and integrating human oversight through techniques like adversarial explanations and criticality analysis. These methods aim to improve AI decision-making, enhance transparency, and mitigate risks associated with unintended or malicious behavior, with applications ranging from scientific instrumentation to complex decision support systems. The field is actively exploring model-based reinforcement learning and language-guided world models to achieve more effective and explainable control.