AI models that create text are influenced by the culture of the data they learn from. This is clear when looking at how these models behave in different languages. Two key aspects of culture were studied: how people relate to others and how they process information.

Social orientation is about whether people see themselves as part of a group or as individuals. Cognitive style is about whether people focus on the big picture or break things down into details. In Chinese, AI shows a stronger sense of belonging to a group and a more holistic view of the world. In English, it leans towards individualism and a more detailed, analytical approach. This was seen in two popular AI models, GPT and ERNIE.

These cultural tendencies have real-world effects. For instance, AI might suggest ads that fit the cultural style of the language it's using. If it's in Chinese, the ads might focus on group harmony. If it's in English, they might highlight personal achievement. This shows how AI can adapt to cultural norms, which is both interesting and a bit concerning.

It's also possible to tweak these cultural tendencies. By giving AI specific cultural prompts, like asking it to think like a Chinese person, its responses can change. This opens up questions about how much we can control AI's cultural biases and whether we should.

The idea that AI can pick up cultural traits is fascinating. It raises important questions about how AI understands and reflects the world. As AI becomes more integrated into daily life, understanding these cultural influences will be crucial. It's a reminder that technology is not neutral; it carries the biases and tendencies of the data it's trained on. This is a complex issue that deserves more attention and discussion.

How Culture Shapes AI's Thinking

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