Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness

Understand

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.