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To tackle the rising phenomenon of hate speech, efforts have been madetowards data curation and analysis. When it comes to analysis of bias, previouswork has focused predominantly on race. In our work, we further investigatebias in hate speech datasets along racial, gender and intersectional axes. Weidentify strong bias against African American English (AAE), masculine andAAE+Masculine tweets, which are annotated as disproportionately more hatefuland offensive than from other demographics. We provide evidence that BERT-basedmodels propagate this bias and show that balancing the training data for theseprotected attributes can lead to fairer models with regards to gender, but notrace.

Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes