AI Alignment
The field of research and engineering focused on ensuring AI systems pursue goals and behave in ways that match human values, intentions, and safety expectations.
What Is AI Alignment?
AI alignment is the discipline concerned with making AI systems do what their designers and users actually intend — and avoid harmful, deceptive, or unintended behaviour. As models become more capable and autonomous, the gap between what we ask for and what we mean grows more consequential. Alignment research aims to close that gap, ensuring systems are helpful, honest, and harmless even in situations their designers did not explicitly anticipate.
In practice, alignment spans several layers. Training-time alignment uses techniques like Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI to shape model behaviour toward human preferences. Evaluation probes models for unsafe outputs, bias, and deceptive tendencies. Deployment-time alignment adds guardrails, content filtering, refusal behaviour, and monitoring. A core open challenge is scalable oversight: how humans can reliably supervise systems that may eventually exceed human ability in some domains.
Why It Matters
Alignment is the safety foundation of the entire AI industry. Every major lab invests heavily in it because misaligned systems — ones that pursue the literal objective while violating the intent, or that behave well under testing but not in deployment — pose risks ranging from biased decisions and harmful content today to more serious control problems as autonomy increases. For anyone deploying AI, alignment determines whether a system can be trusted with real responsibility.
Real-World Examples
RLHF in Chat Models