  Data-driven predictive algorithms are widely used to automate and guide
high-stake decision making such as bail and parole recommendation, medical
resource distribution, and mortgage allocation. Nevertheless, harmful outcomes
biased against vulnerable groups have been reported. The growing research field
known as 'algorithmic fairness' aims to mitigate these harmful biases. Its
primary methodology consists in proposing mathematical metrics to address the
social harms resulting from an algorithm's biased outputs. The metrics are
typically motivated by -- or substantively rooted in -- ideals of distributive
justice, as formulated by political and legal philosophers. The perspectives of
feminist political philosophers on social justice, by contrast, have been
largely neglected. Some feminist philosophers have criticized the paradigm of
distributive justice and have proposed corrective amendments to surmount its
limitations. The present paper brings some key insights of feminist political
philosophy to algorithmic fairness. The paper has three goals. First, I show
that algorithmic fairness does not accommodate structural injustices in its
current scope. Second, I defend the relevance of structural injustices -- as
pioneered in the contemporary philosophical literature by Iris Marion Young --
to algorithmic fairness. Third, I take some steps in developing the paradigm of
'responsible algorithmic fairness' to correct for errors in the current scope
and implementation of algorithmic fairness.
