  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.
