  Abusive language detection models tend to have a problem of being biased
toward identity words of a certain group of people because of imbalanced
training datasets. For example, "You are a good woman" was considered "sexist"
when trained on an existing dataset. Such model bias is an obstacle for models
to be robust enough for practical use. In this work, we measure gender biases
on models trained with different abusive language datasets, while analyzing the
effect of different pre-trained word embeddings and model architectures. We
also experiment with three bias mitigation methods: (1) debiased word
embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a
larger corpus. These methods can effectively reduce gender bias by 90-98% and
can be extended to correct model bias in other scenarios.
