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

TL;DR

This paper challenges Hedden's claim that group fairness constraints are unnecessary by highlighting the moral distinction between predictions about groups and individuals, reaffirming the relevance of fairness metrics.

Contribution

It introduces a moral distinction between human-group-based and human-individual-based predictions, arguing that fairness constraints are necessary for the former but not necessarily for the latter.

Findings

Hedden's argument does not apply to human-group-based predictions.

Group fairness metrics remain relevant for most data science applications.

A moral distinction influences the necessity of fairness constraints.

Abstract

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.