Fairness-aware machine learning has recently attracted various communities to mitigate discrimination against certain societal groups in data-driven tasks. For fair supervised learning, particularly in pre-processing, there have been two main categories: data fairness and task-tailored fairness. The former directly finds an intermediate distribution among the groups, independent of the type of the downstream model, so a learned downstream classification/regression model returns similar predictive scores to individuals inputting the same covariates irrespective of their sensitive attributes. The latter explicitly takes the supervised learning task into account when constructing the pre-processing map. In this work, we study algorithmic fairness for supervised learning and argue that the data fairness approaches impose overly strong regularization from the perspective of the HGR correlation. This motivates us to devise a novel pre-processing approach tailored to supervised learning. We account for the trade-off between fairness and utility in obtaining the pre-processing map. Then we study the behavior of arbitrary downstream supervised models learned on the transformed data to find sufficient conditions to guarantee their fairness improvement and utility preservation. To our knowledge, no prior work in the branch of task-tailored methods has theoretically investigated downstream guarantees when using pre-processed data. We further evaluate our framework through comparison studies based on tabular and image data sets, showing the superiority of our framework which preserves consistent trade-offs among multiple downstream models compared to recent competing models. Particularly for computer vision data, we see our method alters only necessary semantic features related to the central machine learning task to achieve fairness.

翻译：近年来，公平性感知机器学习吸引了多个研究领域的关注，旨在缓解数据驱动任务中对特定社会群体的歧视。在公平监督学习领域，特别是预处理方法中，主要存在两大类别：数据公平性与任务定制化公平性。前者直接寻找群体间的中间分布，其独立于下游模型类型，使得学习到的下游分类/回归模型对输入相同协变量的个体（无论其敏感属性如何）返回相似的预测分数。后者在构建预处理映射时明确考虑监督学习任务。本研究针对监督学习的算法公平性展开分析，指出从HGR相关性视角来看，数据公平性方法施加了过强的正则化约束。这促使我们设计一种专门针对监督学习的创新预处理方法。我们在获取预处理映射时综合考虑公平性与效用性的权衡关系。进而研究基于转换数据学习的任意下游监督模型的行为特征，找出保证其公平性提升与效用保持的充分条件。据我们所知，在任务定制化方法分支中，尚未有先前研究从理论层面探讨使用预处理数据时的下游性能保证。我们进一步通过基于表格数据和图像数据集的对比实验评估所提框架，结果表明：与近期竞争模型相比，我们的框架在多个下游模型间能保持一致的权衡关系，展现出显著优越性。特别针对计算机视觉数据，我们发现本方法仅修改与核心机器学习任务相关的必要语义特征即可实现公平性。