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PreprintCited by 154Ethics and Social Impacts of AICultural Differences and ValuesTopic Modeling

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Which Humans?

Open access
Atari, Mohammad, Xue, Mona J., Park, Peter S., Blasi, Damián Ezequiel, Henrich, Joseph·2023
DOI 10.31234/osf.io/5b26t
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Abstract

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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Research works printed but not linked: 6

  1. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big? . In Proceedings of the 2021 ACM onference on fairness, accountability, and transparency (pp. 610-623).
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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
ORCID
Publisher
Center for Open Science
Published
2023-09-22
DOI
10.31234/osf.io/5b26t
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
Licence
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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