  We investigate the fairness concerns of training a machine learning model
using data with missing values. Even though there are a number of fairness
intervention methods in the literature, most of them require a complete
training set as input. In practice, data can have missing values, and data
missing patterns can depend on group attributes (e.g. gender or race). Simply
applying off-the-shelf fair learning algorithms to an imputed dataset may lead
to an unfair model. In this paper, we first theoretically analyze different
sources of discrimination risks when training with an imputed dataset. Then, we
propose an integrated approach based on decision trees that does not require a
separate process of imputation and learning. Instead, we train a tree with
missing incorporated as attribute (MIA), which does not require explicit
imputation, and we optimize a fairness-regularized objective function. We
demonstrate that our approach outperforms existing fairness intervention
methods applied to an imputed dataset, through several experiments on
real-world datasets.
