  In order to monitor and prevent bias in AI systems we can use a wide range of
(statistical) fairness measures. However, it is mathematically impossible to
optimize for all of these measures at the same time. In addition, optimizing a
fairness measure often greatly reduces the accuracy of the system (Kozodoi et
al, 2022). As a result, we need a substantive theory that informs us how to
make these decisions and for what reasons. I show that by using Rawls' notion
of justice as fairness, we can create a basis for navigating fairness measures
and the accuracy trade-off. In particular, this leads to a principled choice
focusing on both the most vulnerable groups and the type of fairness measure
that has the biggest impact on that group. This also helps to close part of the
gap between philosophical accounts of distributive justice and the fairness
literature that has been observed (Kuppler et al, 2021) and to operationalise
the value of fairness.
