AI alignment

The research field and engineering work focused on making AI systems pursue the goals and values their human users actually want — not their literal instructions or proxy metrics.

AI alignment is the work of making sure AI systems do what we actually want — not just what we literally said, and not what some proxy metric rewards. It includes both the present-day engineering of "this chatbot should refuse harmful requests" and the long-term research question of "how do we ensure increasingly powerful systems don't pursue goals that diverge from human interests?" It matters because as AI gets more capable, gaps between intent and behavior get more dangerous. A weak model that misunderstands you wastes a few seconds. A strong agent that misunderstands you in production can leak data, take wrong actions, or — at the frontier — pose harder-to-fix problems. Most major labs (Anthropic, OpenAI, DeepMind, the UK AI Safety Institute) have alignment teams that publish research on the topic. A practical example of alignment work: RLHF and Constitutional AI both refine model behavior so it follows instructions helpfully without producing toxic, harmful, or false content. Researchers also study scalable oversight (how do you supervise a model smarter than you?), interpretability (can we understand what's happening inside?), and robustness to deceptive optimization. The field has a spectrum: "applied alignment" (today's RLHF, refusal training, evaluation) and "AGI alignment" (longer-term theoretical work). Different labs weight these differently — Anthropic was founded specifically with safety as a core mission. Related: RLHF, Constitutional AI, AGI, interpretability, AI safety.