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.

通过引入多目标优化问题来平衡公平性和数据质量，我们提出了一种找到帕累托最优解的方法，以消除偏见缓解技术对数据集的剔除特定数据点所带来的信任问题，并保证子集对整体人群的公平性、组覆盖率和最小数据损失。