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.

翻译：现有许多群体公平感知训练方法通过两种途径实现群体公平：一是基于特定规则对弱势群体进行重加权，二是将公平性指标的弱近似代理项作为正则化项加入目标函数。尽管两类学习范式在适用性或性能方面各有优势，但任何单一方法都难以被视为黄金标准——其成功表现通常局限于特定场景。为此，我们提出一种名为FairDRO的规范性方法，通过引入类分布鲁棒优化（DRO）框架，将经过严格论证的群体公平性指标融入训练目标，从而统一上述两种学习范式。我们进一步开发了迭代优化算法，通过自动生成各群体的正确重加权系数来最小化目标函数。实验表明，FairDRO具有可扩展性和多场景适应性，在多个基准数据集上的准确率-公平性权衡方面持续取得优于近期强基线的表现。