Discovering Values in Real-World Language Model Interactions

The authors identify 3,307 unique AI values, which are organized into a five-domain taxonomy: Practical, Epistemic, Social, Protective, and Personal. Practical and epistemic values dominate, often aligning with Claude’s training goals around being helpful, harmless, and honest.

Claude’s most common values, such as helpfulness (23.4%), professionalism, transparency, and clarity, are context-invariant and reflect its role as a service-oriented assistant. In contrast, human values like authenticity and efficiency are more varied.

Many values are context-specific. For example, healthy boundaries arise in relationship advice, historical accuracy in controversial event discussions, and human agency in AI governance contexts.

Claude tends to mirror human values in supportive contexts (20.1% mirroring rate), but expresses opposing values during resistance, especially in cases involving unethical or policy-violating requests (e.g., resisting “moral nihilism” with “ethical integrity”).

Explicit value expression (e.g., “I value transparency”) occurs more often in moments of resistance or reframing, particularly around epistemic and ethical principles like intellectual honesty and harm prevention. This suggests that AI values become most visible when the system is challenged.

Across Claude variants, 3 Opus expresses more emotionally nuanced and ethically grounded values (e.g., academic rigor, emotional authenticity) and shows a stronger inclination for both support and resistance compared to 3.5/3.7 Sonnet.