  Classifiers tend to propagate biases present in the data on which they are
trained. Hence, it is important to understand how the demographic identities of
the annotators of comments affect the fairness of the resulting model. In this
paper, we focus on the differences in the ways men and women annotate comments
for toxicity, investigating how these differences result in models that amplify
the opinions of male annotators. We find that the BERT model as-sociates toxic
comments containing offensive words with male annotators, causing the model to
predict 67.7% of toxic comments as having been annotated by men. We show that
this disparity between gender predictions can be mitigated by removing
offensive words and highly toxic comments from the training data. We then apply
the learned associations between gender and language to toxic language
classifiers, finding that models trained exclusively on female-annotated data
perform 1.8% better than those trained solely on male-annotated data and that
training models on data after removing all offensive words reduces bias in the
model by 55.5% while increasing the sensitivity by 0.4%.
