Fairness in Regression -- Analysing a Job Candidates Ranking System

Abstract

Fairness is one of the pillars of any well-functioning society. Recent law-making in the EU regulates the machine-centered approach and thus increases the necessity for certifiable fairness approaches. In this paper, we adapt previous literature on certifiable fairness in classification systems to a regression model for simplified candidates ranking. This model serves as an illustration for an application that should work fairly even if built upon a biased data set. With our synthetic dataset we are able to analyse the challenges of different fairness notions. Although the fairness training manages to improve the certifiable individual fairness, some of the encoded bias remains. We discuss the challenges we faced, including the selection of suitable parameters and the trade off between accuracy and fairness. We hope to encourage more research into fairness improvement and certification, within and beyond group and individual fairness.