Toward Fairness via Maximum Mean Discrepancy Regularization on Logits
  Space
Hao-Wei Chung Ching-Hao Chiu Yu-Jen Chen Yiyu Shi Tsung-Yi Ho
Fairness has become increasingly pivotal in machine learning for high-risk
applications such as machine learning in healthcare and facial recognition.
However, we see the deficiency in the previous logits space constraint methods.
Therefore, we propose a novel framework, Logits-MMD, that achieves the fairness
condition by imposing constraints on output logits with Maximum Mean
Discrepancy. Moreover, quantitative analysis and experimental results show that
our framework has a better property that outperforms previous methods and
achieves state-of-the-art on two facial recognition datasets and one animal
dataset. Finally, we show experimental results and demonstrate that our debias
approach achieves the fairness condition effectively.