Equalizing Financial Impact in Supervised Learning

TL;DR

This paper introduces 'equalized financial impact,' a fairness criterion for supervised learning that aims to address societal issues by ensuring equitable financial outcomes across protected groups, especially in loan decisions.

Contribution

It proposes a modification to existing fairness criteria to better account for societal impacts in financial decision-making contexts.

Findings

Introduces the concept of equalized financial impact.

Provides a theoretical framework for fairness in financial decisions.

Addresses limitations of traditional fairness notions in societal contexts.

Abstract

Notions of "fair classification" that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairness criteria from Hardt, Price, and Srebro [NIPS, 2016] that makes a small step towards addressing this issue in the case of financial decisions like giving loans. We call this new notion "equalized financial impact."