Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2026

Abstract

Existing research on AI cultural biases predominantly focuses on Western models, overlooking critical gaps in non-Western models. We conduct a comparative analysis of AI models – ChatGPT (U.S. developed) and ErnieBot (China developed) – from different cultures to investigate how corresponding cultural biases manifest in their outputs. Additionally, we examine how cultural alignment between human users and AI models impacts their collaborative creative performance and the underlying psychological mechanisms. In Study 1, multi-choice prompt with zero-shot technique was used to evaluate cultural biases in four widely used AI models – ChatGPT-3.5/4, ErnieBot-3.5/4 – comparing their responses to established cultural psychometric scales. We find that both Chinese and American models largely reflect norms, values, and cognitive tendencies consistent with the cultural context of their training data. In Study 2, an experiment showed that when users’ and AI models’ cultural backgrounds align, users demonstrate increased usefulness – but not novelty – in creative solutions for local (but not global) tasks, driven by an enhanced state of psychological flow. These results were replicated in Study 3, a field study using dyad data from supervisor and subordinate reports. Overall, our findings show that AI models developed in different cultural contexts exhibit distinct cultural perspectives and worldviews. This highlights the importance of cultural alignment between users and AI models, particularly during creative collaborations. We discuss the implications of cultural biases in AI, emphasizing the need for cross-cultural AI literacy to improve user interactions and support more effective global AI integration.

Keywords

generative AI, large language models, cultural alignment, creativity, human-AI collaboration

Discipline

Applied Behavior Analysis | Artificial Intelligence and Robotics | Organizational Behavior and Theory

Research Areas

Integrative Research Areas

Publication

PNAS Nexus

ISSN

2752-6542

Identifier

10.1093/pnasnexus/pgag084

Publisher

Oxford University Press

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1093/pnasnexus/pgag084

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