  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?
