  Most fair machine learning methods either highly rely on the sensitive
information of the training samples or require a large modification on the
target models, which hinders their practical application. To address this
issue, we propose a two-stage training algorithm named FAIRIF. It minimizes the
loss over the reweighted data set (second stage) where the sample weights are
computed to balance the model performance across different demographic groups
(first stage). FAIRIF can be applied on a wide range of models trained by
stochastic gradient descent without changing the model, while only requiring
group annotations on a small validation set to compute sample weights.
Theoretically, we show that, in the classification setting, three notions of
disparity among different groups can be mitigated by training with the weights.
Experiments on synthetic data sets demonstrate that FAIRIF yields models with
better fairness-utility trade-offs against various types of bias; and on
real-world data sets, we show the effectiveness and scalability of FAIRIF.
Moreover, as evidenced by the experiments with pretrained models, FAIRIF is
able to alleviate the unfairness issue of pretrained models without hurting
their performance.
