Tao Yan, Viberg Olga, Baker Ryan S, Kizilcec René F
Department of Information Science, Cornell University, Ithaca, NY 14853, USA.
Department of Human Centered Technology, KTH Royal Institute of Technology, Stockholm 10044, Sweden.
PNAS Nexus. 2024 Sep 17;3(9):pgae346. doi: 10.1093/pnasnexus/pgae346. eCollection 2024 Sep.
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 later 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.
文化从根本上塑造了人们的推理、行为和交流方式。随着人们越来越多地使用生成式人工智能(AI)来加速和自动化个人及专业任务,人工智能模型中嵌入的文化价值观可能会使人们的真实表达产生偏差,并助长某些文化的主导地位。我们通过将五个广泛使用的大语言模型(OpenAI的GPT-4o/4-turbo/4/3.5-turbo/3)的回答与具有全国代表性的调查数据进行比较,对文化偏差进行了分类评估。所有模型都表现出类似于英语国家和新教欧洲国家的文化价值观。我们测试了文化提示作为一种控制策略,以提高每个国家/地区的文化契合度。对于较新的模型(GPT-4、4-turbo、4o),这提高了71%-81%的国家和地区的模型输出的文化契合度。我们建议使用文化提示和持续评估来减少生成式人工智能输出中的文化偏差。