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Generative artificial intelligence (AI) models trained on cultural text show distinct cultural biases in different languages. These AI cultural tendencies influence social orientation and cognitive styles, impacting real-world applications like advertising.

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Area of Science:

  • Artificial Intelligence
  • Cultural Psychology
  • Computational Linguistics

Background:

  • Generative AI models are trained on vast textual datasets, which inherently contain cultural information.
  • Cultural psychology studies how social orientation and cognitive styles vary across cultures.
  • Understanding AI's cultural tendencies is crucial for its ethical and effective deployment.

Purpose of the Study:

  • To investigate whether generative AI models exhibit cultural tendencies when processing different human languages.
  • To analyze AI's social orientation and cognitive style across linguistic and cultural contexts.
  • To explore the real-world impact and potential mitigation strategies for AI's cultural biases.

Main Methods:

  • Analysis of GPT model responses to standardized psychological measures in Chinese and English.
  • Replication of findings using ERNIE, a prominent Chinese generative AI model.
  • Evaluation of AI-generated advertisements for cultural orientation in different languages.

Main Results:

  • GPT demonstrated a more interdependent social orientation and holistic cognitive style in Chinese compared to English.
  • These cultural tendencies were replicated in the ERNIE model.
  • AI-driven advertising recommendations showed corresponding cultural biases, favoring interdependent orientations in Chinese contexts.

Conclusions:

  • Generative AI models trained on cultural text inherit and exhibit these cultural tendencies.
  • AI's cultural biases manifest in social orientation, cognitive style, and practical applications.
  • Cultural prompts can potentially modulate these AI cultural tendencies, suggesting avenues for bias mitigation.