Fair Classifiers Without Fair Training: An Influence-Guided Data
  Sampling Approach
Jinlong Pang Jialu Wang Zhaowei Zhu Yuanshun Yao Chen Qian Yang Liu
A fair classifier should ensure the benefit of people from different groups,
while the group information is often sensitive and unsuitable for model
training. Therefore, learning a fair classifier but excluding sensitive
attributes in the training dataset is important. In this paper, we study
learning fair classifiers without implementing fair training algorithms to
avoid possible leakage of sensitive information. Our theoretical analyses
validate the possibility of this approach, that traditional training on a
dataset with an appropriate distribution shift can reduce both the upper bound
for fairness disparity and model generalization error, indicating that fairness
and accuracy can be improved simultaneously with simply traditional training.
We then propose a tractable solution to progressively shift the original
training data during training by sampling influential data, where the sensitive
attribute of new data is not accessed in sampling or used in training.
Extensive experiments on real-world data demonstrate the effectiveness of our
proposed algorithm.