On the Apparent Conflict Between Individual and Group Fairness

A distinction has been drawn in fair machine learning research between'group' and'individual' fairness measures. Many tec hnical research papers assume that both are important, but conflict ing, and propose ways to minimise the tradeoffs between these mea - sures. 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 fun da-mentally in conflict. First, it outlines accounts of egalita rian fairness which encompass plausible motivations for both group a nd 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 conclusi on is that the apparent conflict between individual and group fair ness is more of an artefact 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 ca refully justified application of measures to mitigate it.