Reducing Gender Bias in Abusive Language Detection

TL;DR

This paper investigates gender bias in abusive language detection models, evaluates bias across datasets and architectures, and proposes mitigation techniques that significantly reduce bias, enhancing model robustness.

Contribution

It introduces three effective bias mitigation methods—debiased embeddings, gender swap augmentation, and large corpus fine-tuning—for abusive language detection.

Findings

Bias reduction of 90-98% achieved

Bias varies with datasets and models

Mitigation methods are broadly applicable

Abstract

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.