Values in the Wild: Discovering and Mapping Values in Real-World Language Model Interactions

AI assistants interact with millions of real users everyday, imparting normative judgments that can have significant personal and societal impact—but little is known about what values guide these interactions in practice. To address this, we develop a method to empirically analyze values expressed in hundreds of thousands of real-world conversations with Claude models. We empirically discover and taxonomize 3,308 AI values, and study how model values and responses depend on context. We find that Claude expresses many professional and intellectual values, and typically supports prosocial human values while resisting values like "moral nihilism." While some values appear consistently (e.g. "professionalism"), most are highly context-dependent—"harm prevention" emerges when the model resists users, "historical accuracy" when discussing controversial events, "healthy boundaries" in relationship advice, and "human agency" in technology ethics discussions. By providing the first large-scale empirical mapping of AI values in deployment, this work creates a foundation for more grounded evaluation and design of values in increasingly influential AI systems.