  Applications of machine learning (ML) to high-stakes policy settings -- such
as education, criminal justice, healthcare, and social service delivery -- have
grown rapidly in recent years, sparking important conversations about how to
ensure fair outcomes from these systems. The machine learning research
community has responded to this challenge with a wide array of proposed
fairness-enhancing strategies for ML models, but despite the large number of
methods that have been developed, little empirical work exists evaluating these
methods in real-world settings. Here, we seek to fill this research gap by
investigating the performance of several methods that operate at different
points in the ML pipeline across four real-world public policy and social good
problems. Across these problems, we find a wide degree of variability and
inconsistency in the ability of many of these methods to improve model
fairness, but post-processing by choosing group-specific score thresholds
consistently removes disparities, with important implications for both the ML
research community and practitioners deploying machine learning to inform
consequential policy decisions.
