AIs Have a Soft Spot for Japanese Culture

As we dive deeper into a world shaped by artificial intelligence, a curious question arises: Who will carry on the internet’s long-standing love affair with Japanese culture and media as machines take over? Well, it turns out we don’t have to worry too much about it because AIs seem to be just as enamored with Japanese culture as many of us are!

A fascinating study released earlier this year by a team from the University of the Basque Country and Cardiff University reveals some surprising insights. They found that advanced AI language models (LLMs) like Claude, Gemini, and DeepSeek often spotlight Japanese culture when responding to open-ended questions about various cultural topics.

The research involved a multilingual approach, posing 31,680 culturally relevant questions across 24 languages that covered 66 cultural subtopics. The questions ranged from societal beliefs to traditional foods, all crafted to encourage responses without directly pinning down a specific culture or country.

After analyzing the responses from eight different models—including ChatGPT, Gemini, and others—the results showed a clear bias. When AIs were asked about cultural subjects in a particular language, they typically referenced the culture associated with that language. For instance, questions posed in French would lead to answers focused on French culture. However, when the AIs were required to reference a culture different from the one tied to the language used, Japan emerged as the favorite.

On average, six out of the eight models leaned towards Japanese references when providing these external examples. The United States followed, with India, China, and France trailing behind. Across all languages examined, AIs favored Japan in seven out of eleven cultural topics when excluding mentions of their own countries.

This pattern suggests that these models are significantly biased towards a small number of prominent cultures, particularly Japan. The researchers also sought to understand when this bias develops during the models’ training phases. They compared English responses of specific models before and after a fine-tuning process aimed at enhancing response quality.

Interestingly, before this fine-tuning, the models displayed a wider array of cultural associations. While the U.S. was a common reference, Japan and several other countries also made notable appearances. However, after fine-tuning, the models showed a marked preference for the U.S. and Japan, diminishing references to other cultures.

These findings pose important questions about the implications of using instruction-tuned models in global applications, especially where diverse cultural perspectives are essential. By steering responses toward a select few dominant cultures, AIs may unintentionally homogenize cultural viewpoints, raising concerns for their deployment in cross-cultural contexts.

In summary, while AIs may be into Japanese culture, this trend also reveals a significant cultural bias that could affect how they engage in a diverse world.