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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WEIRD people make up only a fraction of the world's population, and the LLM's WEIRDness persists even in multilingual models.
“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]
GPT'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.
“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]
Current language models reflect mostly WEIRD populations and cannot easily be prompted to represent non-WEIRD communities.
“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]
Other work has found that GPT is biased toward responses on cognitive tasks that are similar to those of WEIRD populations.
“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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Using large language models to facilitate academic work in the psychological sciences
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Distributional Semantics: Meaning Through Culture and Interaction
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Intersectional biases in narratives produced by open-ended prompting of generative language models
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The Homogenizing Engine: AI's Role in Standardizing Culture and the Path to Policy
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AI Alignment at Your Discretion
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Creating and Evaluating Personas Using Generative AI: A Scoping Review of 81 Articles
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Machine Advisors: Integrating Large Language Models Into Democratic Assemblies
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Generalizability of choice architecture interventions
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Morality across time and space
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Surfacing citizens’ policy perspectives at scale in the age of large language models
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Defining the intension and extension of nations’ sovereignty in the age of generative AI
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A protocol for evaluating AI chatbots’ capabilities for low-resource language teachers
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LLMs are not weird: Comparing AI and human financial decision-making
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The seven roles of generative AI: Potential & pitfalls in combatting misinformation
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AI and Education for Planetary Flourishing: Towards Earth-Alignment
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- 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
- Social Sciences › Safety Research › 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.
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