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A recent paper (Hedden 2021) has argued that most of the group fairnessconstraints discussed in the machine learning literature are not necessaryconditions for the fairness of predictions, and hence that there are no genuinefairness metrics. This is proven by discussing a special case of a fairprediction. In our paper, we show that Hedden 's argument does not hold for themost common kind of predictions used in data science, which are about peopleand based on data from similar people; we call these human-group-basedpractices. We argue that there is a morally salient distinction betweenhuman-group-based practices and those that are based on data of only oneperson, which we call human-individual-based practices. Thus, what may be anecessary condition for the fairness of human-group-based practices may not bea necessary condition for the fairness of human-individual-based practices, onwhich Hedden 's argument is based. Accordingly, the group fairness metricsdiscussed in the machine learning literature may still be relevant for mostapplications of prediction-based decision making.

People are not coins. Morally distinct types of predictions necessitate different fairness constraints