Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory

TL;DR

This paper offers a moral analysis of statistical fairness criteria in machine learning, arguing that the fairness of calibration and error rate equality depends on context and should not be universally mandated.

Contribution

It provides a nuanced moral perspective on fairness criteria, emphasizing the context-dependent nature of calibration and error rate equality in algorithmic fairness.

Findings

Group calibration may be unfair in some contexts but not in others.

Fairness requirements are context-sensitive and cannot be universally applied.

Arguments for fairness based on calibration or error rate equality do not always generalize.

Abstract

In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of false positive and false negative rates between groups. In our paper, we focus on moral arguments in support of either measure. The conflict between group calibration vs. false positive and false negative rate equality is one of the core issues in the debate about group fairness definitions among practitioners. For any thorough moral analysis, the meaning of the term fairness has to be made explicit and defined properly. For our paper, we equate fairness with (non-)discrimination, which is a legitimate understanding in the discussion about group fairness. More specifically, we equate it with prima facie wrongful discrimination in the sense this is used in Prof. Lippert-Rasmussen's treatment of this definition. In this paper, we argue that a violation of group calibration may be unfair in some cases, but not unfair in others. This is in line with claims already advanced in the literature, that algorithmic fairness should be defined in a way that is sensitive to context. The most important practical implication is that arguments based on examples in which fairness requires between-group calibration, or equality in the false-positive/false-negative rates, do no generalize. For it may be that group calibration is a fairness requirement in one case, but not in another.