Priority-based Post-Processing Bias Mitigation for Individual and Group
  Fairness
  Previous post-processing bias mitigation algorithms on both group and
individual fairness don't work on regression models and datasets with
multi-class numerical labels. We propose a priority-based post-processing bias
mitigation on both group and individual fairness with the notion that similar
individuals should get similar outcomes irrespective of socio-economic factors
and more the unfairness, more the injustice. We establish this proposition by a
case study on tariff allotment in a smart grid. Our novel framework establishes
it by using a user segmentation algorithm to capture the consumption strategy
better. This process ensures priority-based fair pricing for group and
individual facing the maximum injustice. It upholds the notion of fair tariff
allotment to the entire population taken into consideration without modifying
the in-built process for tariff calculation. We also validate our method and
show superior performance to previous work on a real-world dataset in criminal
sentencing.
