Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairness, previous work in data valuation does not consider how training instances may influence both performance and fairness of ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data valuation framework that can be used to incorporate fairness concerns into a series of ML-related tasks (e.g., data pre-processing, exploratory data analysis, active learning). We propose an entropy-based data valuation metric suited to address our two-pronged goal of maximizing both performance and fairness, which is more computationally efficient than existing metrics. We then show how FADO can be applied as the basis for unfairness mitigation pre-processing techniques. Our methods achieve promising results -- up to a 40 p.p. improvement in fairness at a less than 1 p.p. loss in performance compared to a baseline -- and promote fairness in a data-centric way, where a deeper understanding of data quality takes center stage.

翻译：数据估值是机器学习领域中研究训练实例对给定预测任务价值的分支。尽管数据偏差是下游模型不公平性的主要来源之一，但以往的数据估值工作并未考虑训练实例如何同时影响机器学习模型的性能与公平性。为此，我们提出公平感知数据估值框架（FADO），该框架可将公平性考量融入一系列机器学习相关任务（如数据预处理、探索性数据分析、主动学习）。我们提出一种基于熵的数据估值度量，旨在兼顾性能与公平性的双重目标，且相比现有度量具有更高的计算效率。进而展示FADO如何作为基于数据预处理的不公平缓解技术的基础。我们的方法取得了显著成果——与基线相比，在性能损失低于1个百分点的情况下，公平性提升高达40个百分点——并以数据为中心的方式促进公平性，其中对数据质量的深入理解成为核心关注点。