  While the field of algorithmic fairness has brought forth many ways to
measure and improve the fairness of machine learning models, these findings are
still not widely used in practice. We suspect that one reason for this is that
the field of algorithmic fairness came up with a lot of definitions of
fairness, which are difficult to navigate. The goal of this paper is to provide
data scientists with an accessible introduction to group fairness metrics and
to give some insight into the philosophical reasoning for caring about these
metrics. We will do this by considering in which sense socio-demographic groups
are compared for making a statement on fairness.
