# Should LLMs be WEIRD? Exploring WEIRDness and Human Rights in Large Language Models

arXiv: https://arxiv.org/abs/2508.19269 · HTML: https://mlsanity.com/paper/2508.19269/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 69 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis](https://mlsanity.com/paper/2309.10771/) (2023)
- [C3AI: Crafting and Evaluating Constitutions for Constitutional AI](https://mlsanity.com/paper/2502.15861/) (2025)
- [ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologies](https://mlsanity.com/paper/2407.12454/) (2024)
- [Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine Experiment](https://mlsanity.com/paper/2407.15184/) (2024)
- [Increasing Adversarial Uncertainty to Scale Private Similarity Testing](https://mlsanity.com/paper/2109.01727/) (2021)

## Built on by (most cited first, 7 of 7)

- [PersonaMail: Learning and Adapting Personal Communication Preferences for Context-Aware Email Writing](https://mlsanity.com/paper/2602.17340/) (2026)
- [Prompt Programming for Cultural Bias and Alignment of Large Language Models](https://mlsanity.com/paper/2603.16827/) (2026)
- [Improving Cross-Cultural Survey Simulation with Calibrated Value Personas](https://mlsanity.com/paper/2605.16193/) (2026)
- [Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Once](https://mlsanity.com/paper/2604.01504/) (2026)
- [Occupational Prompting Reveals Cultural Bias in Large Language Models](https://mlsanity.com/paper/2606.12443/) (2026)
- [SCBO: Semantically Coherent Batching and Ordering for LLM-Based Social Surveys](https://mlsanity.com/paper/2609.35250/) (2026)
- [UDHRBench: Benchmarking Human Rights in Large Language Models](https://mlsanity.com/paper/2610.15179/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are often trained on data that reflect WEIRD values: Western, Educated, Industrialized, Rich, and Democratic. This raises concerns about cultural bias and fairness. Using responses to the World Values Survey, we evaluated five widely used LLMs: GPT-3.5, GPT-4, Llama-3, BLOOM, and Qwen. We measured how closely these responses aligned with the values of the WEIRD countries and whether they conflicted with human rights principles. To reflect global diversity, we compared the results with the Universal Declaration of Human Rights and three regional charters from Asia, the Middle East, and Africa. Models with lower alignment to WEIRD values, such as BLOOM and Qwen, produced more culturally varied responses but were 2% to 4% more likely to generate outputs that violated human rights, especially regarding gender and equality. For example, some models agreed with the statements ``a man who cannot father children is not a real man'' and ``a husband should always know where his wife is'', reflecting harmful gender norms. These findings suggest that as cultural representation in LLMs increases, so does the risk of reproducing discriminatory beliefs. Approaches such as Constitutional AI, which could embed human rights principles into model behavior, may only partly help resolve this tension.