It is well known that AI-based language technology -- large language models, machine translation systems, multilingual dictionaries, and corpora -- is currently limited to 2 to 3 percent of the world's most widely spoken and/or financially and politically best supported languages. In response, recent research efforts have sought to extend the reach of AI technology to ``underserved languages.'' In this paper, we show that many of these attempts produce flawed solutions that adhere to a hard-wired representational preference for certain languages, which we call techno-linguistic bias. Techno-linguistic bias is distinct from the well-established phenomenon of linguistic bias as it does not concern the languages represented but rather the design of the technologies. As we show through the paper, techno-linguistic bias can result in systems that can only express concepts that are part of the language and culture of dominant powers, unable to correctly represent concepts from other communities. We argue that at the root of this problem lies a systematic tendency of technology developer communities to apply a simplistic understanding of diversity which does not do justice to the more profound differences that languages, and ultimately the communities that speak them, embody. Drawing on the concept of epistemic injustice, we point to the broader sociopolitical consequences of the bias we identify and show how it can lead not only to a disregard for valuable aspects of diversity but also to an under-representation of the needs and diverse worldviews of marginalized language communities.

翻译：众所周知，基于AI的语言技术（包括大型语言模型、机器翻译系统、多语言词典及语料库）目前仅覆盖全球最广泛使用和/或最具财政及政治支持的语言的2%至3%。为此，近年研究致力于将AI技术拓展至“未得到充分支持的语言”。本文指出，此类尝试中许多方案存在缺陷，其设计遵循一种对特定语言的固化表征偏好，我们称之为“技术语言偏见”。技术语言偏见与已广泛认知的语言偏见不同，它并非关注被表征的语言本身，而在于技术的设计方式。通过本文论证，技术语言偏见可能导致系统仅能表达主导权力主体的语言与文化概念，无法正确表征其他社群的概念。我们认为，该问题的根源在于技术开发者社群系统性地倾向于对多样性进行简单化理解，未能公正对待语言（及最终使用这些语言的社群）所体现的深层差异。基于认知不公的概念，我们揭示了所识别偏见更广泛的社会政治后果，并阐明其不仅导致多样性宝贵维度的忽视，更造成边缘化语言社群需求及多元世界观的表征不足。