Balancing Fairness and Accuracy in Data-Restricted Binary Classification

arXiv:2403.07724v1 Announce Type: new
Abstract: Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier. For example, in some applications, a classifier may not have direct access to sensitive attributes, affecting its ability to produce accurate and fair decisions. This paper proposes a framework that models the trade-off between accuracy and fairness under four practical scenarios that dictate the type of data available for analysis. Prior works examine this trade-off …

abstract accuracy applications arxiv binary classification classifier cs.ai cs.cy cs.lg data deal decisions example fair fairness information machine machine learning paper restrictions stat.ml type