A comparative study of fairness-enhancing interventions in machine learning

TLDR

Computers increasingly make high‑impact decisions, yet their predictions can disproportionately affect different population subgroups, prompting growing interest in fairness‑enhanced classifiers. This study compares various fairness‑enhancing techniques, investigates the factors driving their differences, and highlights overlooked aspects essential for their widespread adoption.

Abstract

Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions that require investigation for these algorithms to receive broad adoption.