  Existing approaches to mitigate demographic biases evaluate on monolingual
data, however, multilingual data has not been examined. In this work, we treat
the gender as domains (e.g., male vs. female) and present a standard domain
adaptation model to reduce the gender bias and improve performance of text
classifiers under multilingual settings. We evaluate our approach on two text
classification tasks, hate speech detection and rating prediction, and
demonstrate the effectiveness of our approach with three fair-aware baselines.
