Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes
This addresses bias in hate speech detection for marginalized groups, but is incremental as it extends prior race-focused work to include gender and intersectionality.
The paper tackled bias in hate speech datasets by analyzing it along racial, gender, and intersectional axes, finding strong bias against African American English, masculine, and intersectional tweets, and showed that balancing training data improved fairness for gender but not race.
To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis. When it comes to analysis of bias, previous work has focused predominantly on race. In our work, we further investigate bias in hate speech datasets along racial, gender and intersectional axes. We identify strong bias against African American English (AAE), masculine and AAE+Masculine tweets, which are annotated as disproportionately more hateful and offensive than from other demographics. We provide evidence that BERT-based models propagate this bias and show that balancing the training data for these protected attributes can lead to fairer models with regards to gender, but not race.