A Systematic Approach to Group Fairness in Automated Decision Making

TL;DR

This paper provides an accessible overview of group fairness metrics in algorithmic decision making, clarifying their philosophical basis and practical relevance for data scientists.

Contribution

It offers a systematic, understandable introduction to various group fairness definitions and explores their philosophical motivations.

Findings

Clarifies differences between fairness metrics

Highlights practical challenges in applying fairness measures

Provides insights into the ethical considerations of fairness

Abstract

While the field of algorithmic fairness has brought forth many ways to measure and improve the fairness of machine learning models, these findings are still not widely used in practice. We suspect that one reason for this is that the field of algorithmic fairness came up with a lot of definitions of fairness, which are difficult to navigate. The goal of this paper is to provide data scientists with an accessible introduction to group fairness metrics and to give some insight into the philosophical reasoning for caring about these metrics. We will do this by considering in which sense socio-demographic groups are compared for making a statement on fairness.