Optimal Recourse Summaries via Bi-Objective Decision Tree Learning

TL;DR

This paper introduces SOGAR, a decision tree-based method for creating recourse summaries that balance effectiveness and cost, enabling global bias detection and comparison across subgroups.

Contribution

SOGAR formulates recourse summary learning as a Pareto optimization problem, providing a flexible, post-hoc selection of trade-offs with improved stability and performance.

Findings

SOGAR outperforms existing methods in effectiveness and cost metrics.

It produces stable, low-cost, and effective recourse summaries.

SOGAR finds the Pareto front of solutions for optimal trade-offs.

Abstract

Actionable Recourse provides individuals with actions they can take to change an unfavorable classifier outcome. While useful at the instance level, it is ill-suited for global auditing and bias detection, since aggregating local actions is costly and often inconsistent. Recourse Summaries address this limitation by partitioning the population and assigning one shared action per subgroup, enabling comparison across subgroups. Designing summaries involves a fundamental trade-off between recourse effectiveness and recourse cost, which existing methods do not adequately address. We introduce Summaries of Optimal and Global Actionable Recourse (SOGAR), which formulates recourse summary learning as an optimal decision tree learning problem and finds the Pareto front -- the complete set of solutions where improving one objective necessarily worsens the other. SOGAR enables post-hoc selection of the desired trade-off without retraining. Using shallow axis-parallel decision trees and sparse leaf actions, SOGAR produces stable, low-cost, and effective recourse summaries that outperform existing approaches across effectiveness and cost metrics.