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.

翻译：随着语言模型日益普及，解决其对不同人口群体和因素的不公平对待变得至关重要。大多数关于评估和缓解公平性危害的研究集中在英语领域，而多语言模型和非英语语言受到的关注相对较少。本文调查了英语以外语言及多语言环境中公平性的不同方面，呈现了对多语言和非英语背景下公平性的综述，突出了当前研究的不足以及为英语设计的方法所面临的困难。我们认为，世界各地多元化的文化和语言使得构建公平性数据集时实现全面覆盖不可行。因此，偏差的测量和缓解必须超越当前以数据集为驱动的实践，这些实践狭隘地聚焦于特定维度和类型的偏差，因而无法跨语言和文化进行扩展。