提出评估跨文化偏见的社会影响框架，推动更负责任的AI语言技术发展。
On The Conceptualization and Societal Impact of Cross-Cultural Bias
- 梳理2025年20篇相关论文，提炼偏见识别与评估的共性方法
- 强调忽视真实使用者会掩盖技术在跨文化场景中的实际危害
- 呼吁建立可落地的社会影响评估体系，适合关注AI伦理的研究者
研究表明，尽管大语言模型（LLMs）能依据文化语境生成回应，但仍存在跨文化泛化问题。然而，在评估语言技术的文化偏见时，研究者常忽略真实使用者，从而回避了核心问题。受arXiv:2005.14050v2启发，本文分析了2025年发表的20篇关于自然语言处理（NLP）中文化偏见识别与评估的文献，提炼出一系列观察，旨在帮助未来NLP研究者更具体地概念化偏见，并有效评估其社会危害。目标是倡导对展现跨文化偏见的语言技术进行稳健的社会影响评估。
原文摘要 · Abstract (English)
Research has shown that while large language models (LLMs) can generate their responses based on cultural context, they are not perfect and tend to generalize across cultures. However, when evaluating the cultural bias of a language technology on any dataset, researchers may choose not to engage with stakeholders actually using that technology in real life, which evades the very fundamental problem they set out to address. Inspired by the work done by arXiv:2005.14050v2, I set out to analyse recent literature about identifying and evaluating cultural bias in Natural Language Processing (NLP). I picked out 20 papers published in 2025 about cultural bias and came up with a set of observations to allow NLP researchers in the future to conceptualize bias concretely and evaluate its harms effectively. My aim is to advocate for a robust assessment of the societal impact of language technologies exhibiting cross-cultural bias.
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