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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]
In the citing paper
“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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Li, L., Mo, C., Zhou, H., Yu, H., Lu, C., Wang, S., Athreya, V. M., Fitzgerald, M. B., & Wang, S. X. (2026) · Journal of Medical Internet Research · doi:10.2196/94755 · Cited by 2
WHY: Perspective: POZE—A Multidisciplinary Framework of Life
Walther, C. C. (2024) · Springer eBooks · doi:10.1007/978-3-031-67823-3_1 · Cited by 2
Artificial moral intelligence and computability: an Aristotelian perspective
Kyriacou, C. (2024) · AI and Ethics · doi:10.1007/s43681-024-00543-1 · Cited by 2
ChatGPT and CLT: Investigating differences in multimodal processing
Cahalane, M. C., & Kirshner, S. N. (2024) · Journal of Economy and Technology · doi:10.1016/j.ject.2024.11.008 · Cited by 2
Turing Jest: Distributional Semantics and One‐Line Jokes
Trott, S., Walker, D. E., Taylor, S. M., & Seana Coulson, A. (2025) · Cognitive Science · doi:10.1111/cogs.70066 · Cited by 3
Artificial Intelligence Chatbots Mimic Human Collective BehaviourPreprint
He, J. K., Wallis, F. P. S., Gvirtz, A., & Rathje, S. (2023) · doi:10.31234/osf.io/vf5ap · Cited by 2
Mapping the Mind With Free Associations: A Tutorial Using the R Package associatoRPreprint
Aeschbach, S., Mata, R., & Wulff, D. U. (2024) · doi:10.31234/osf.io/ra87s · Cited by 2
Bias, accuracy, and trust: no GenAI in peer reviewing
Salem, D. B., Barrat, J., Ammari, S., Lojo‐Lendoiro, S., Belaid, A., Attyé, A., Ducloyer, M., Edjlali, M., Boutet, C., & Ognard, J. (2025) · Journal of Neuroradiology · doi:10.1016/j.neurad.2025.101411 · Cited by 2
Schimmelpfennig, R., Díaz, M., Prabhakaran, V., & Davani, A. (2025) · arXiv · doi:10.48550/arxiv.2512.17898 · Cited by 2
Taming the AI tools – education first, technology second
Loble, L., & Stephens, K. (2025) · Theory Into Practice · doi:10.1080/00405841.2025.2528546 · Cited by 1
Large AI Models Have a Prioritization Problem: Policy Implications and Solutions
Jackson, J. C., Liu, Y., Wang, Z., & Brady, W. J. (2025) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322251408311 · Cited by 1
Feng, Z., Milewicz, R. M., Murphy-Hill, E. R., Menezes, T., Serebrenik, A., Steinmacher, I., & Sarma, A. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3789210 · Cited by 7
Asadi, A. R. (2026) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3786304.3787936 · Cited by 1
Park, S., Kim, J. H., Kim, H., Bin, H., Moon, S. B., & Lee, W. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792728 · Cited by 2
AI and Education for Planetary Flourishing: Towards Earth-Alignment
Stein, S. (2026) · Postdigital Science and Education · doi:10.1007/s42438-026-00643-7 · Cited by 3
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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