Fairness in Language Models Beyond English: Gaps and Challenges

Krithika Ramesh, Sunayana Sitaram, M. Choudhury
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引用次数: 8

Abstract

With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors. Most research on evaluating and mitigating fairness harms has been concentrated on English, while multilingual models and non-English languages have received comparatively little attention. In this paper, we survey different aspects of fairness in languages beyond English and multilingual contexts. This paper presents a survey of fairness in multilingual and non-English contexts, highlighting the shortcomings of current research and the difficulties faced by methods designed for English. We contend that the multitude of diverse cultures and languages across the world makes it infeasible to achieve comprehensive coverage in terms of constructing fairness datasets. Thus, the measurement and mitigation of biases must evolve beyond the current dataset-driven practices that are narrowly focused on specific dimensions and types of biases and, therefore, impossible to scale across languages and cultures.
英语之外的语言模式公平:差距与挑战
随着语言模型变得越来越普遍,解决它们对不同人口群体和因素的不公平对待变得至关重要。评估和减轻公平损害的研究大多集中在英语领域,而多语言模型和非英语语言受到的关注相对较少。在本文中,我们调查了英语和多语言语境之外的语言中公平的不同方面。本文对多语言和非英语环境下的公平进行了调查,强调了当前研究的不足和为英语设计的方法所面临的困难。我们认为,世界各地的多种文化和语言使得在构建公平数据集方面实现全面覆盖是不可能的。因此,衡量和减轻偏见必须超越目前数据集驱动的做法,这些做法狭隘地关注特定的维度和类型的偏见,因此不可能跨语言和文化进行扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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