Measuring Geographic Performance Disparities of Offensive Language Classifiers
This work addresses bias in text classifiers for offensive language detection, which is crucial for fair AI applications, though it is incremental in measuring disparities rather than solving them.
The paper tackled the problem of geographic performance disparities in offensive language classifiers by analyzing a novel dataset across 15 cities, finding that current models do not generalize across locations and produce false positives on African American English without correlation to minority population proportions.
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.