  A growing specter in the rise of machine learning is whether the decisions
made by machine learning models are fair. While research is already underway to
formalize a machine-learning concept of fairness and to design frameworks for
building fair models with sacrifice in accuracy, most are geared toward either
supervised or unsupervised learning. Yet two observations inspired us to wonder
whether semi-supervised learning might be useful to solve discrimination
problems. First, previous study showed that increasing the size of the training
set may lead to a better trade-off between fairness and accuracy. Second, the
most powerful models today require an enormous of data to train which, in
practical terms, is likely possible from a combination of labeled and unlabeled
data. Hence, in this paper, we present a framework of fair semi-supervised
learning in the pre-processing phase, including pseudo labeling to predict
labels for unlabeled data, a re-sampling method to obtain multiple fair
datasets and lastly, ensemble learning to improve accuracy and decrease
discrimination. A theoretical decomposition analysis of bias, variance and
noise highlights the different sources of discrimination and the impact they
have on fairness in semi-supervised learning. A set of experiments on
real-world and synthetic datasets show that our method is able to use unlabeled
data to achieve a better trade-off between accuracy and discrimination.

Machine Learning;Machine Learning