Does Claude's Constitution Have a Culture?
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Abstract (arXiv)
Constitutional AI (CAI) aligns language models with explicitly stated normative principles, offering a transparent alternative to implicit alignment through human feedback alone. However, because constitutions are authored by specific groups of people, the resulting models may reflect particular cultural perspectives. We investigate this question by evaluating Anthropic's Claude Sonnet on 55 World Values Survey items, selected for high cross-cultural variance across six value domains and administered as both direct survey questions and naturalistic advice-seeking scenarios. Comparing Claude's responses to country-level data from 90 nations, we find that Claude's value profile most closely resembles those of Northern European and Anglophone countries, but on a majority of items extends beyond the range of all surveyed populations. When users provide cultural context, Claude adjusts its rhetorical framing but not its substantive value positions, with effect sizes indistinguishable from zero across all twelve tested countries. An ablation removing the system prompt increases refusals but does not alter the values expressed when responses are given, and replication on a smaller model (Claude Haiku) confirms the same cultural profile across model sizes. These findings suggest that when a constitution is authored within the same cultural tradition that dominates the training data, constitutional alignment may codify existing cultural biases rather than correct them--producing a value floor that surface-level interventions cannot meaningfully shift. We discuss the compounding nature of this risk and the need for globally representative constitution-authoring processes.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 30 Mar 2026.
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References · 15
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2023Whose Opinions Do Language Models Reflect?cited by 759
- 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 424
- 2023Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Studycited by 203
- 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
- 2023Cultural Bias and Cultural Alignment of Large Language Modelscited by 149
- 2022Probing Pre-Trained Language Models for Cross-Cultural Differences in Valuescited by 147
- 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 136
- 2024Collective Constitutional AI: Aligning a Language Model with Public Inputcited by 120
- 2022The Ghost in the Machine has an American accent: value conflict in GPT-3cited by 105
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