We propose a novel reduction-to-binary (R2B) approach that enforces demographic parity for multiclass classification with non-binary sensitive attributes via a reduction to a sequence of binary debiasing tasks. We prove that R2B satisfies optimality and bias guarantees and demonstrate empirically that it can lead to an improvement over two baselines: (1) treating multiclass problems as multi-label by debiasing labels independently and (2) transforming the features instead of the labels. Surprisingly, we also demonstrate that independent label debiasing yields competitive results in most (but not all) settings. We validate these conclusions on synthetic and real-world datasets from social science, computer vision, and healthcare.

翻译：我们建议一种新型的减少二元制(R2B)方法,通过减少一系列二元性贬低任务,对非二元性敏感属性的多级分类实行人口均等,我们证明R2B符合最佳性和偏向性保障,并用经验证明这可以导致两个基线的改进:(1) 独立地将多级问题作为多级标签处理,(2) 改变特征而不是标签。令人惊讶的是,我们还证明,独立标签贬低在多数(但并非全部)环境中产生竞争结果。我们验证了有关合成和真实世界社会科学、计算机视觉和保健数据集的这些结论。