  Recent conversations in the algorithmic fairness literature have raised
several concerns with standard conceptions of fairness. First, constraining
predictive algorithms to satisfy fairness benchmarks may lead to non-optimal
outcomes for disadvantaged groups. Second, technical interventions are often
ineffective by themselves, especially when divorced from an understanding of
structural processes that generate social inequality. Inspired by both these
critiques, we construct a common decision-making model, using mortgage loans as
a running example. We show that under some conditions, any choice of decision
threshold will inevitably perpetuate existing disparities in financial
stability unless one deviates from the Pareto optimal policy. Then, we model
the effects of three different types of interventions. We show how different
interventions are recommended depending upon the difficulty of enacting
structural change upon external parameters and depending upon the policymaker's
preferences for equity or efficiency. Counterintuitively, we demonstrate that
preferences for efficiency over equity may lead to recommendations for
interventions that target the under-resourced group. Finally, we simulate the
effects of interventions on a dataset that combines HMDA and Fannie Mae loan
data. This research highlights the ways that structural inequality can be
perpetuated by seemingly unbiased decision mechanisms, and it shows that in
many situations, technical solutions must be paired with external,
context-aware interventions to enact social change.
