Alignment Science | behavior.engineering

Alignment science is the academic and research side of the broader alignment problem — developing the theoretical foundations and empirical methods for understanding how to make AI systems do what their designers intend, even as they become more capable and are applied to harder tasks. This includes research into reward modeling, interpretability (understanding what’s happening inside a model), scalable oversight (how humans can supervise AI that’s more capable than they are), and formal approaches to specifying goals. For behavior architects, alignment science is the upstream research that shapes the tools and techniques available to them: methods like RLHF and Constitutional AI originated in alignment research and became practical tools that practitioners now use daily.

Value AlignmentThe degree to which a model's behavior reflects human values, intentions, and goals rather than optimizing for narrow objectives that miss the point.
AI SafetyThe field concerned with ensuring that AI systems behave in ways that are safe, controllable, and aligned with human values — especially as they become more capable.
RLHF (Reinforcement Learning from Human Feedback)A way of training AI models by having humans rate or compare outputs, then using those ratings to reinforce better behavior over time.
Constitutional AIAn approach developed by Anthropic where a model is trained to critique and revise its own outputs based on a written set of principles.
Responsible AIA framework and practice for developing and deploying AI systems in ways that are safe, fair, transparent, and accountable.