  A recent paper (Hedden 2021) has argued that most of the group fairness
constraints discussed in the machine learning literature are not necessary
conditions for the fairness of predictions, and hence that there are no genuine
fairness metrics. This is proven by discussing a special case of a fair
prediction. In our paper, we show that Hedden 's argument does not hold for the
most common kind of predictions used in data science, which are about people
and based on data from similar people; we call these human-group-based
practices. We argue that there is a morally salient distinction between
human-group-based practices and those that are based on data of only one
person, which we call human-individual-based practices. Thus, what may be a
necessary condition for the fairness of human-group-based practices may not be
a necessary condition for the fairness of human-individual-based practices, on
which Hedden 's argument is based. Accordingly, the group fairness metrics
discussed in the machine learning literature may still be relevant for most
applications of prediction-based decision making.
