In this paper we revisit the bias-variance decomposition of model error from the perspective of designing a fair classifier: we are motivated by the widely held socio-technical belief that noise variance in large datasets in social domains tracks demographic characteristics such as gender, race, disability, etc. We propose a conditional-iid (ciid) model built from group-specific classifiers that seeks to improve on the trade-offs made by a single model (iid setting). We theoretically analyze the bias-variance decomposition of different models in the Gaussian Mixture Model, and then empirically test our setup on the COMPAS and folktables datasets. We instantiate the ciid model with two procedures that improve "fairness" by conditioning out undesirable effects: first, by conditioning directly on sensitive attributes, and second, by clustering samples into groups and conditioning on cluster membership (blind to protected group membership). Our analysis suggests that there might be principled procedures and concrete real-world use cases under which conditional models are preferred, and our striking empirical results strongly indicate that non-iid settings, such as the ciid setting proposed here, might be more suitable for big data applications in social contexts.

翻译：本文从设计公平分类器的视角重新审视模型误差的偏差-方差分解：我们受一种广泛持有的社会技术信念启发，即社会领域大数据中的噪声方差会追踪人口统计特征（如性别、种族、残疾等）。我们提出一种基于组别特异分类器的条件独立同分布（ciid）模型，旨在改进单一模型（iid设定）所面临的权衡。我们从理论上分析了高斯混合模型中不同模型的偏差-方差分解，随后在COMPAS和folktables数据集上进行了实证测试。我们通过两种程序实例化ciid模型以提升“公平性”：其一直接对敏感属性进行条件化，其二将样本聚类为组别并对聚类成员身份进行条件化（对受保护群体身份不可见）。我们的分析表明，在某些原则性程序和具体现实应用场景下，条件模型可能更受青睐；而我们所获得的显著实证结果强烈表明，非iid设定（如本文提出的ciid设定）可能更适用于社会情境中的大数据应用。