  Most fair regression algorithms mitigate bias towards sensitive sub
populations and therefore improve fairness at group level. In this paper, we
investigate the impact of such implementation of fair regression on the
individual. More precisely, we assess the evolution of continuous predictions
from an unconstrained to a fair algorithm by comparing results from baseline
algorithms with fair regression algorithms for the same data points. Based on
our findings, we propose a set of post-processing algorithms to improve the
utility of the existing fair regression approaches.
