Structural Interventions and the Dynamics of Inequality

TL;DR

This paper examines how structural inequalities persist despite algorithmic fairness efforts, demonstrating that technical interventions alone are insufficient without addressing underlying social processes, and proposing context-aware policy interventions.

Contribution

It introduces a decision-making model illustrating the limitations of fairness constraints and evaluates different intervention strategies considering structural social factors.

Findings

Decision thresholds can perpetuate disparities without structural change.

Interventions vary based on external change feasibility and policy preferences.

Combining technical and structural interventions is necessary for social equity.

Abstract

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.