Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization
This addresses fairness in AI for underrepresented groups, offering a scalable and adaptable solution, though it appears incremental as it builds on existing schemes.
The authors tackled the problem of achieving group fairness in machine learning by proposing FairDRO, a method that unifies re-weighting and regularization schemes using classwise distributionally robust optimization, resulting in state-of-the-art performance on benchmark datasets in terms of accuracy-fairness trade-off.
Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strength in terms of applicability or performance, respectively, it is difficult for any method in the either category to be considered as a gold standard since their successful performances are typically limited to specific cases. To that end, we propose a principled method, dubbed as \ours, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective using a class wise distributionally robust optimization (DRO) framework. We then develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines.