We address the problem of fair classification in settings where data is scarce and unbalanced across demographic groups. Such low-data regimes are common in domains like medical imaging, where false negatives can have fatal consequences. We propose a novel approach \emph{OxEnsemble} for efficiently training ensembles and enforcing fairness in these low-data regimes. Unlike other approaches, we aggregate predictions across ensemble members, each trained to satisfy fairness constraints. By construction, \emph{OxEnsemble} is both data-efficient -- carefully reusing held-out data to enforce fairness reliably -- and compute-efficient, requiring little more compute than used to fine-tune or evaluate an existing model. We validate this approach with new theoretical guarantees. Experimentally, our approach yields more consistent outcomes and stronger fairness-accuracy trade-offs than existing methods across multiple challenging medical imaging classification datasets.

翻译：我们研究了数据稀缺且在不同人口群体间分布不均衡场景下的公平分类问题。这种低数据情形常见于医疗影像等领域，其中假阴性可能导致致命后果。我们提出了一种新颖方法——OxEnsemble，用于在低数据场景中高效训练集成模型并实现公平性约束。与其他方法不同，我们聚合各集成成员的预测结果，每个成员均经过训练以满足公平性约束。通过设计，OxEnsemble兼具数据高效性——通过精心复用保留数据可靠地实现公平性约束，以及计算高效性——所需计算量仅略高于微调或评估现有模型。我们通过新的理论保证验证了该方法的有效性。实验表明，在多个具有挑战性的医疗影像分类数据集上，我们的方法相较于现有方法产生了更一致的输出结果和更优的公平-准确率权衡。