  Inspired by recent breakthroughs in predictive modeling, practitioners in
both industry and government have turned to machine learning with hopes of
operationalizing predictions to drive automated decisions. Unfortunately, many
social desiderata concerning consequential decisions, such as justice or
fairness, have no natural formulation within a purely predictive framework. In
efforts to mitigate these problems, researchers have proposed a variety of
metrics for quantifying deviations from various statistical parities that we
might expect to observe in a fair world and offered a variety of algorithms in
attempts to satisfy subsets of these parities or to trade off the degree to
which they are satisfied against utility. In this paper, we connect this
approach to \emph{fair machine learning} to the literature on ideal and
non-ideal methodological approaches in political philosophy. The ideal approach
requires positing the principles according to which a just world would operate.
In the most straightforward application of ideal theory, one supports a
proposed policy by arguing that it closes a discrepancy between the real and
the perfectly just world. However, by failing to account for the mechanisms by
which our non-ideal world arose, the responsibilities of various
decision-makers, and the impacts of proposed policies, naive applications of
ideal thinking can lead to misguided interventions. In this paper, we
demonstrate a connection between the fair machine learning literature and the
ideal approach in political philosophy, and argue that the increasingly
apparent shortcomings of proposed fair machine learning algorithms reflect
broader troubles faced by the ideal approach. We conclude with a critical
discussion of the harms of misguided solutions, a reinterpretation of
impossibility results, and directions for future research.
