In this paper, we deal with bias mitigation techniques that remove specific data points from the training set to aim for a fair representation of the population in that set. Machine learning models are trained on these pre-processed datasets, and their predictions are expected to be fair. However, such approaches may exclude relevant data, making the attained subsets less trustworthy for further usage. To enhance the trustworthiness of prior methods, we propose additional requirements and objectives that the subsets must fulfill in addition to fairness: (1) group coverage, and (2) minimal data loss. While removing entire groups may improve the measured fairness, this practice is very problematic as failing to represent every group cannot be considered fair. In our second concern, we advocate for the retention of data while minimizing discrimination. By introducing a multi-objective optimization problem that considers fairness and data loss, we propose a methodology to find Pareto-optimal solutions that balance these objectives. By identifying such solutions, users can make informed decisions about the trade-off between fairness and data quality and select the most suitable subset for their application.

翻译：本文研究了一种通过从训练集中移除特定数据点来追求该集合中群体公平表征的偏差缓解技术。机器学习模型在这些预处理后的数据集上进行训练，其预测结果预期具有公平性。然而，此类方法可能会排除相关数据，导致所得子集的后续可用性可信度降低。为了增强现有方法的可信度，我们提出了子集除公平性外还需满足的额外要求与目标：(1) 群体覆盖性，以及(2) 最小数据损失。虽然移除整个群体可能改善测量的公平性，但这种做法存在严重问题——未能表征每个群体本身就不能被视为公平。针对第二个关注点，我们主张在最小化歧视的同时保留数据。通过引入一个同时考虑公平性和数据损失的多目标优化问题，我们提出了一种方法学来寻找平衡这两个目标的帕累托最优解。通过识别此类解，用户可就公平性与数据质量之间的权衡做出明智决策，并选择最适合其应用场景的子集。