When Fairness Metrics Help Each Other: Look-Ahead Optimization for Multi-Metric Fairness Weighting

Abstract

Fairness-aware machine learning depends crucially on the fairness criterion chosen, and it is well understood that these criteria are often in conflict with each other. In this work, we challenge this conventional wisdom by exploring how competing fairness criteria may in some cases interact in helpful ways. We investigate whether adding an auxiliary fairness objective can improve a primary fairness objective while maintaining or improving predictive performance. We refer to such cases as fairness-metric synergies, and identify them using a Pareto improvement criterion. We first use fixed grid search to examine when these improvements appear across standard fairness metrics and socially consequential binary classification datasets, demonstrating that this phenomenon can indeed occur. To make this method more practical, we then propose LA-FW (Look-Ahead Fairness Weighting), a modular dynamic weighting algorithm that searches for useful fairness-weight configurations through short look-ahead updates during training. We analyze the computational relationship between LA-FW and fixed grid search and show that fixed grid search can be understood as a limiting case when candidate weights are evaluated with sufficient computation. Our experiments compare fixed grid search and LA-FW in terms of fairness, accuracy, and runtime, demonstrating substantial speedups while producing competitive fairness results across three datasets. Overall, this work adds to an emerging understanding that fairness metric trade-offs are more nuanced than was previously believed.