  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.
