Value Alignment

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Value alignment deals with the gap between what an AI system is told to do and what people actually care about. Many human values depend on context and judgment, which makes them difficult to express as fixed rules or numbers. A system can follow instructions perfectly and still behave in ways that feel wrong if those values were never clearly reflected in how it was designed.

Work in this area starts by deciding which values matter for a given system and who gets to define them. Some teams try to learn these values from human feedback or examples of preferred behavior. Others set clear boundaries by writing rules about what the system must not do. When values conflict, decisions about trade-offs need to be made explicit and recorded. This work continues after release, since expectations and social norms change, and systems must be adjusted to stay aligned over time.

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