On the Apparent Conflict Between Individual and Group Fairness

TL;DR

This paper challenges the perceived conflict between individual and group fairness in machine learning, arguing that they are compatible in principle and that conflicts arise from simplistic applications of fairness measures.

Contribution

It provides a theoretical analysis showing that individual and group fairness are not inherently conflicting, drawing from political philosophy and legal theory to clarify their relationship.

Findings

No fundamental conflict between individual and group fairness in principle

Misconceptions lead to perceived conflicts in fairness measures

Nuanced, context-aware application of fairness measures can resolve conflicts

Abstract

A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on a misconception. It draws on theoretical discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fundamentally in conflict. First, it outlines accounts of egalitarian fairness which encompass plausible motivations for both group and individual fairness, thereby suggesting that there need be no conflict in principle. Second, it considers the concept of individual justice, from legal philosophy and jurisprudence which seems similar but actually contradicts the notion of individual fairness as proposed in the fair machine learning literature. The conclusion is that the apparent conflict between individual and group fairness is more of an artifact of the blunt application of fairness measures, rather than a matter of conflicting principles. In practice, this conflict may be resolved by a nuanced consideration of the sources of `unfairness' in a particular deployment context, and the carefully justified application of measures to mitigate it.