Implementing Fair Regression In The Real World
It addresses fairness in regression for real-world applications, focusing on individual-level impacts, which is an incremental improvement over existing group-level methods.
The paper investigates how fair regression algorithms affect individual predictions, comparing baseline and fair algorithms, and proposes post-processing methods to enhance utility.
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.