The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Which Humans?
Open accessAbstract
Large language models (LLMs) have recently made vast advances in both generating and analyzing textual data. Technical reports often compare LLMs’ outputs with “human” performance on various tests. Here, we ask, “Which humans?” Much of the existing literature largely ignores the fact that humans are a cultural species with substantial psychological diversity around the globe that is not fully captured by the textual data on which current LLMs have been trained. We show that LLMs’ responses to psychological measures are an outlier compared with large-scale cross-cultural data, and that their performance on cognitive psychological tasks most resembles that of people from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies but declines rapidly as we move away from these populations (r = -.70). Ignoring cross-cultural diversity in both human and machine psychology raises numerous scientific and ethical issues. We close by discussing ways to mitigate the WEIRD bias in future generations of generative language models.
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“However, WEIRD people make up only a fraction of the world's population, and the LLM's WEIRDness persists even in multilingual models.”
From A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure (2025) · cites this paper as Atari et al. [2]
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“The model's responses most resemble a cluster of WEIRD countries including the USA, Canada, Northern Ireland, New Zealand, Great Britain, Australia, Andorra, Germany, and the Netherlands.”
From A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure (2025) · cites this paper as Atari et al. [2]
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“Indeed, current language models reflect mostly WEIRD (Western Educated Industrialized Rich Democratic) populations and cannot easily be prompted to represent non-WEIRD communities (66, 67).”
From How should the advancement of large language models affect the practice of science? (2025) · cites this paper as [67]
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“Other work has found that GPT is biased toward responses on cognitive tasks that are similar to those of WEIRD populations (78).”
From GPT is an effective tool for multilingual psychological text analysis (2024) · cites this paper as [78]
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Metadata
- Authors
- Atari, M., Xue, M. J., Park, P. S., Blasi, D. E., & Henrich, J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Atari, Mohammad
- Publisher
- Center for Open Science
- Published
- 2023-09-22
- OpenAlex
- W4386981810
- Cited by
- 151
- References
- 50
- Citations by year
- 2026: 59 · 2025: 50 · 2024: 38 · 2023: 4
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Funders
- John Templeton Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Cultural Differences and Values
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, WEIRD bias, cross-cultural diversity, psychological diversity, generative language models, bias mitigation
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