Measuring justice in machine learning

TL;DR

This paper examines how to measure justice in machine learning, comparing Rawls's theory with capability-based approaches, and argues that current fair ML practices may be using an inadequate measure of justice.

Contribution

It critically analyzes the application of Rawls's theory in fair ML and highlights the importance of choosing the appropriate measure of justice, considering capability theory.

Findings

Rawls's theory may encode biases against disabled individuals.

Capability theory offers an alternative measure of justice.

Current fair ML practices might be using an inadequate justice measure.

Abstract

How can we build more just machine learning systems? To answer this question, we need to know both what justice is and how to tell whether one system is more or less just than another. That is, we need both a definition and a measure of justice. Theories of distributive justice hold that justice can be measured (in part) in terms of the fair distribution of benefits and burdens across people in society. Recently, the field known as fair machine learning has turned to John Rawls's theory of distributive justice for inspiration and operationalization. However, philosophers known as capability theorists have long argued that Rawls's theory uses the wrong measure of justice, thereby encoding biases against people with disabilities. If these theorists are right, is it possible to operationalize Rawls's theory in machine learning systems without also encoding its biases? In this paper, I draw on examples from fair machine learning to suggest that the answer to this question is no: the capability theorists' arguments against Rawls's theory carry over into machine learning systems. But capability theorists don't only argue that Rawls's theory uses the wrong measure, they also offer an alternative measure. Which measure of justice is right? And has fair machine learning been using the wrong one?