On Consequentialism and Fairness

TL;DR

This paper critically examines the ethical foundations of fairness in machine learning through a consequentialist lens, highlighting tradeoffs, limitations, and implications for automated decision-making systems.

Contribution

It offers a consequentialist critique of existing fairness definitions and discusses the ethical considerations of learning and randomization in machine learning.

Findings

Highlights tradeoffs in fairness definitions

Critiques current approaches from a consequentialist perspective

Discusses implications of learning and randomization for ethics

Abstract

Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is consequentialism, the position that, roughly speaking, outcomes are all that matter. Although consequentialism is not free from difficulties, and although it does not necessarily provide a tractable way of choosing actions (because of the combined problems of uncertainty, subjectivity, and aggregation), it nevertheless provides a powerful foundation from which to critique the existing literature on machine learning fairness. Moreover, it brings to the fore some of the tradeoffs involved, including the problem of who counts, the pros and cons of using a policy, and the relative value of the distant future. In this paper we provide a consequentialist critique of common definitions of fairness within machine learning, as well as a machine learning perspective on consequentialism. We conclude with a broader discussion of the issues of learning and randomization, which have important implications for the ethics of automated decision making systems.