Group-blind optimal transport to group parity and its constrained
  variants
Quan Zhou Jakub Marecek
Fairness holds a pivotal role in the realm of machine learning, particularly
when it comes to addressing groups categorised by sensitive attributes, e.g.,
gender, race. Prevailing algorithms in fair learning predominantly hinge on
accessibility or estimations of these sensitive attributes, at least in the
training process. We design a single group-blind projection map that aligns the
feature distributions of both groups in the source data, achieving
(demographic) group parity, without requiring values of the protected attribute
for individual samples in the computation of the map, as well as its use.
Instead, our approach utilises the feature distributions of the privileged and
unprivileged groups in a boarder population and the essential assumption that
the source data are unbiased representation of the population. We present
numerical results on synthetic data and real data.