Spatial Flip: A Postprocessing Method to Improve Spatial Fairness

In this study, we examine how to improve the Spatial Fairness of a classifier by selecting suitable modifications, flips, to its output. When Machine Learning algorithms are evaluated for Spatial Fairness and found to be unfair, it is important to make corrections to prevent the development of products and services that discriminate against individuals based on their location. We compared five different strategies for improving the Spatial Fairness of a classifier by computing observations' features. Among these strategies, an ensemble approach proved to be the most effective by modifying points among the recommendations made by simpler strategies.