Latent Bias Mitigation via Contrastive Learning
Yue Gao, Shu Zhang, Jun Sun, Shan Shan Yu, Akihito Yoshii · 2024
Recently, Deep Neural Networks (DNN) shows potential in many applications, while the risks behind DNN hinder its wide deployment. One of the most prominent risks of DNN is its fairness issue and it is necessary to mitigate the biases behind DNN to make it more trustworthy. Existing bias mitigation methods for learning fair feature representations trained from large-scale datasets through the correlation between the target attribute and sensitive attribute. However, these methods require many human annotations and ignore many latent biased attributes that are difficult to realize and discover by human means. To solve these problems, we propose a novel latent bias mitigation method, and the main idea is as follows: (1) amplification of a biased model without any sensitive attribute labels; (2) proposal of a Fair Contrastive Learning Loss to encourage learning of fair representations and discourage learning of biased information. Experimental results show that our method outperforms state-of-the-art benchmarks in term of latent bias mitigation, as well as achieving better accuracy.