Papers › Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

13 May 2020ACL 2020 6arXiv:2005.06251archive 2025-07-28

Shengyu Jia, Tao Meng, Jieyu Zhao, Kai-Wei Chang

Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., Zhao et al. (2017) show that these techniques inadvertently capture the societal bias hidden in the corpus and further amplify it. However, their analysis is conducted only on models' top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.

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