Related papers: Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

URL: http://arxiv.org/abs/2005.06251v1
Date: Wed, 13 May 2020 11:07:10 GMT
Title: Mitigating Gender Bias Amplification in Distribution by Posterior Regularization
Authors: Shengyu Jia, Tao Meng, Jieyu Zhao and Kai-Wei Chang
Abstract summary: We investigate the gender bias amplification issue from the distribution perspective. We propose a bias mitigation approach based on posterior regularization. Our study sheds the light on understanding the bias amplification.
Score: 75.3529537096899
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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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