Fairness has become a trending topic in natural language processing (NLP),
which addresses biases targeting certain social groups such as genders and
religions. However, regional bias in language models (LMs), a long-standing
global discrimination problem, still remains unexplored. This paper bridges the
gap by analysing the regional bias learned by the pre-trained language models
that are broadly used in NLP tasks. In addition to verifying the existence of
regional bias in LMs, we find that the biases on regional groups can be
strongly influenced by the geographical clustering of the groups. We
accordingly propose a HiErarchical Regional Bias evaluation method (HERB)
utilising the information from the sub-region clusters to quantify the bias in
pre-trained LMs. Experiments show that our hierarchical metric can effectively
evaluate the regional bias with respect to comprehensive topics and measure the
potential regional bias that can be propagated to downstream tasks. Our codes
are available at this https URL.

本文针对自然语言处理中存在的公平性问题，提出了一种基于 HERB 方法的区域偏差评估方法，通过对 LMs 学习到的区域偏差进行分析，验证了其存在性，并发现集团地理聚集性对 LMs 的区域偏差具有显著影响。实验结果表明，我们的 hierarchical metric 方法可以有效评估 LMs 的区域偏差，并测量可能传播到下游任务的潜在区域偏差。

HERB：衡量预训练语言模型中的层次区域偏差

HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models

Text classifiers are applied at scale in the form of one-size-fits-all
solutions. Nevertheless, many studies show that classifiers are biased
regarding different languages and dialects. When measuring and discovering
these biases, some gaps present themselves and should be addressed. First,
``Does language, dialect, and topical content vary across geographical
regions?'' and secondly ``If there are differences across the regions, do they
impact model performance?''. We introduce a novel dataset called GeoOLID with
more than 14 thousand examples across 15 geographically and demographically
diverse cities to address these questions. We perform a comprehensive analysis
of geographical-related content and their impact on performance disparities of
offensive language detection models. Overall, we find that current models do
not generalize across locations. Likewise, we show that while offensive
language models produce false positives on African American English, model
performance is not correlated with each city's minority population proportions.
Warning: This paper contains offensive language.

研究发现，在不同的语言和方言中，文本分类器存在偏见。本文介绍了一个新的数据集 GeoOLID，以及基于该数据集进行的地域相关内容以及其对模型性能差异的完整分析。研究表明，当前模型不能跨位置推广，并且尽管针对非洲裔美国英语的攻击语言模型会引起虚假警报，但是模型性能与每个城市的少数族裔人口比例没有相关性。