AI alignment is the field concerned with making AI systems do what we actually want, not just what we literally said. As models become more capable, the gap between "what was specified" and "what was intended" can widen, and the consequences of misalignment grow. Alignment research covers reinforcement learning from human feedback (RLHF), constitutional AI, interpretability (understanding what a model is doing internally), and red-teaming (stress-testing models for unsafe behavior).
Modern alignment techniques include: (1) RLHF, where humans rate model outputs and the model is trained to produce highly-rated responses; (2) Constitutional AI, where the model is trained against a written set of principles, often via self-critique; (3) Reward modeling, where a separate model learns to predict human preferences; (4) Interpretability research, which tries to understand the model's internal computations to detect dangerous reasoning patterns before they manifest in outputs.
Alignment is partly technical, partly governance, and partly philosophical. The hardest cases are ones where the right behavior is genuinely contested.
As AI systems take on consequential tasks (running code, managing money, writing legal documents), alignment moves from research curiosity to engineering requirement. A misaligned agent that has tools is meaningfully different from a misaligned chatbot.
The Claude Mythos article touches on Anthropic's alignment work and how it shapes the assistant's behavior in practice.