  In current hate speech datasets, there exists a high correlation between
annotators' perceptions of toxicity and signals of African American English
(AAE). This bias in annotated training data and the tendency of machine
learning models to amplify it cause AAE text to often be mislabeled as
abusive/offensive/hate speech with a high false positive rate by current hate
speech classifiers. In this paper, we use adversarial training to mitigate this
bias, introducing a hate speech classifier that learns to detect toxic
sentences while demoting confounds corresponding to AAE texts. Experimental
results on a hate speech dataset and an AAE dataset suggest that our method is
able to substantially reduce the false positive rate for AAE text while only
minimally affecting the performance of hate speech classification.
