FairRR: Pre-Processing for Group Fairness through Randomized Response

arXiv:2403.07780v1 Announce Type: cross
Abstract: The increasing usage of machine learning models in consequential decision-making processes has spurred research into the fairness of these systems. While significant work has been done to study group fairness in the in-processing and post-processing setting, there has been little that theoretically connects these results to the pre-processing domain. This paper proposes that achieving group fairness in downstream models can be formulated as finding the optimal design matrix in which to modify a response variable …

abstract arxiv cs.lg decision fairness machine machine learning machine learning models making post-processing pre-processing processes processing research results stat.ml study systems through type usage work