What is AI Alignment?
The challenge of ensuring AI systems pursue goals that are beneficial to humans, rather than goals that are misspecified, harmful, or contrary to human values.
Definition
AI alignment is the technical and philosophical challenge of ensuring that AI systems do what humans actually want — not just what they are literally instructed to do. As AI systems become more capable and autonomous, misaligned goals become increasingly dangerous. Alignment research encompasses: reward modeling (specifying what "good" means), interpretability (understanding what models have learned), robustness (ensuring models behave safely in edge cases), and governance (policy and deployment standards).
Why it matters
Alignment is why Anthropic built Claude the way it did — Constitutional AI and reinforcement from human feedback are alignment techniques. As AI becomes more capable, alignment matters more: a misaligned powerful AI is far more dangerous than a misaligned weak one. PMs and leaders deploying AI need to understand alignment to make responsible product decisions and evaluate vendor safety commitments.
How it works
Current alignment techniques: (1) RLHF (Reinforcement Learning from Human Feedback) — humans rate outputs; the model is trained to produce highly-rated outputs. (2) Constitutional AI (Anthropic) — the model critiques its own outputs against a set of principles. (3) Red-teaming — adversarial testing to find cases where the model behaves harmfully. (4) Interpretability — analyzing model internals to understand what concepts the model has learned.
Examples in practice
Claude's Constitutional AI
Anthropic trains Claude with a set of principles (the "Constitution") and has the model evaluate and revise its own responses against those principles — producing a model that is more reliably helpful and less harmful than one trained on human feedback alone.