AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances

Dhruv Agarwal, Mor Naaman, Aditya Vashistha
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Abstract

Large language models (LLMs) are being increasingly integrated into everyday products and services, such as coding tools and writing assistants. As these embedded AI applications are deployed globally, there is a growing concern that the AI models underlying these applications prioritize Western values. This paper investigates what happens when a Western-centric AI model provides writing suggestions to users from a different cultural background. We conducted a cross-cultural controlled experiment with 118 participants from India and the United States who completed culturally grounded writing tasks with and without AI suggestions. Our analysis reveals that AI provided greater efficiency gains for Americans compared to Indians. Moreover, AI suggestions led Indian participants to adopt Western writing styles, altering not just what is written but also how it is written. These findings show that Western-centric AI models homogenize writing toward Western norms, diminishing nuances that differentiate cultural expression.
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人工智能建议使写作趋同于西方风格,削弱了文化的细微差别
大型语言模型(LLM)正被越来越多地集成到日常产品和服务中,如编码工具和写作助手。随着这些嵌入式人工智能应用在全球范围内的部署,人们越来越担心这些应用背后的人工智能模型会优先考虑西方价值观。本文研究了当以西方为中心的人工智能模型向来自不同文化背景的用户提供写作建议时会发生什么。我们对来自印度和美国的 118 名参与者进行了跨文化对照实验,他们在有人工智能建议和没有人工智能建议的情况下完成了具有文化基础的写作任务。我们的分析表明,与印度人相比,人工智能为美国人带来了更高的效率。此外,人工智能建议导致印度参与者采用西方写作风格,不仅改变了写作内容,还改变了写作方式。这些研究结果表明,以西方为中心的人工智能模式将写作同质化,使其趋向于西方规范,减少了文化表达的细微差别。
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