Cultural Bias and Cultural Alignment of Large Language Models
This addresses cultural bias in AI models that can affect users' authentic expression and contribute to cultural dominance, offering a practical control strategy.
The study evaluated cultural bias in five large language models, finding they all exhibit values resembling English-speaking and Protestant European countries, and tested cultural prompting to improve alignment, achieving better alignment for 71-81% of countries and territories with recent models.
Culture fundamentally shapes people's reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people's authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI's GPT-4o/4-turbo/4/3.5-turbo/3) by comparing the models' responses to nationally representative survey data. All models exhibit cultural values resembling English-speaking and Protestant European countries. We test cultural prompting as a control strategy to increase cultural alignment for each country/territory. For recent models (GPT-4, 4-turbo, 4o), this improves the cultural alignment of the models' output for 71-81% of countries and territories. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI.