Cultural tendencies in generative AI

Jackson G. Lu (), Lesley Luyang Song and Lu Doris Zhang
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Jackson G. Lu: Massachusetts Institute of Technology, MIT Sloan School of Management
Lesley Luyang Song: Tongji University, Advanced Institute of Business
Lu Doris Zhang: Massachusetts Institute of Technology, MIT Sloan School of Management

Nature Human Behaviour, 2025, vol. 9, issue 11, 2360-2369

Abstract: Abstract We show that generative artificial intelligence (AI) models—trained on textual data that are inherently cultural—exhibit cultural tendencies when used in different human languages. Here we focus on two foundational constructs in cultural psychology: social orientation and cognitive style. First, we analyse GPT’s responses to a large set of measures in both Chinese and English. When used in Chinese (versus English), GPT exhibits a more interdependent (versus independent) social orientation and a more holistic (versus analytic) cognitive style. Second, we replicate these cultural tendencies in ERNIE, a popular generative AI model in China. Third, we demonstrate the real-world impact of these cultural tendencies. For example, when used in Chinese (versus English), GPT is more likely to recommend advertisements with an interdependent (versus independent) social orientation. Fourth, exploratory analyses suggest that cultural prompts (for example, prompting generative AI to assume the role of a Chinese person) can adjust these cultural tendencies.

Date: 2025
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DOI: 10.1038/s41562-025-02242-1

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