Procedural Fairness in Machine Learning

arXiv:2404.01877v1 Announce Type: new
Abstract: Fairness in machine learning (ML) has received much attention. However, existing studies have mainly focused on the distributive fairness of ML models. The other dimension of fairness, i.e., procedural fairness, has been neglected. In this paper, we first define the procedural fairness of ML models, and then give formal definitions of individual and group procedural fairness. We propose a novel metric to evaluate the group procedural fairness of ML models, called $GPF_{FAE}$, which utilizes a …

abstract arxiv attention cs.lg definitions fairness however machine machine learning ml models paper studies type