  Presence of bias (in datasets or tasks) is inarguably one of the most
critical challenges in machine learning applications that has alluded to
pivotal debates in recent years. Such challenges range from spurious
associations between variables in medical studies to the bias of race in gender
or face recognition systems. Controlling for all types of biases in the dataset
curation stage is cumbersome and sometimes impossible. The alternative is to
use the available data and build models incorporating fair representation
learning. In this paper, we propose such a model based on adversarial training
with two competing objectives to learn features that have (1) maximum
discriminative power with respect to the task and (2) minimal statistical mean
dependence with the protected (bias) variable(s). Our approach does so by
incorporating a new adversarial loss function that encourages a vanished
correlation between the bias and the learned features. We apply our method to
synthetic data, medical images (containing task bias), and a dataset for gender
classification (containing dataset bias). Our results show that the learned
features by our method not only result in superior prediction performance but
also are unbiased. The code is available at
https://github.com/QingyuZhao/BR-Net/.
