Fairness in machine learning has become a critical concern. Existing approaches often focus on achieving full fairness across all score ranges generated by predictive models, ensuring fairness in both high- and low-percentile populations. However, this stringent requirement can compromise predictive performance and may not align with the practical fairness concerns of stakeholders. In this work, we propose a novel framework for building partially fair machine learning models that enforce fairness only within a specific percentile interval of interest while maintaining flexibility in other regions. We introduce statistical metrics to evaluate partial fairness within a given percentile interval. To achieve partial fairness, we propose an in-processing method by formulating the model training problem as constrained optimization with difference-of-convex constraints, which can be solved by an inexact difference-of-convex algorithm (IDCA). We provide the complexity analysis of IDCA for finding a nearly KKT point. Through numerical experiments on real-world datasets, we demonstrate that our framework achieves high predictive performance while enforcing partial fairness where it matters most.

翻译：机器学习中的公平性已成为一个关键问题。现有方法通常致力于在预测模型生成的所有分数范围内实现完全公平，确保高百分位和低百分位群体均享公平性。然而，这种严格的要求可能会损害预测性能，且未必符合利益相关方实际关注的公平性问题。本研究提出一种构建部分公平机器学习模型的新框架，该框架仅在特定百分位数区间内强制实现公平性，同时保持其他区域的灵活性。我们引入了统计指标以评估给定百分位数区间内的部分公平性。为实现部分公平性，我们提出了一种内处理法，将模型训练问题建模为带有凸差约束的约束优化，并可通过非精确凸差算法（IDCA）求解。我们提供了IDCA在寻找近似KKT点时的复杂度分析。通过真实世界数据集的数值实验，我们证明该框架能够在强制实现最相关区域公平性的同时保持高预测性能。