Algorithmic fairness is of utmost societal importance, yet the current trend in large-scale machine learning models requires training with massive datasets that are frequently biased. In this context, pre-processing methods that focus on modeling and correcting bias in the data emerge as valuable approaches. In this paper, we propose FairShap, a novel instance-level data re-weighting method for fair algorithmic decision-making through data valuation by means of Shapley Values. FairShap is model-agnostic and easily interpretable, as it measures the contribution of each training data point to a predefined fairness metric. We empirically validate FairShap on several state-of-the-art datasets of different nature, with a variety of training scenarios and models and show how it yields fairer models with similar levels of accuracy than the baselines. We illustrate FairShap's interpretability by means of histograms and latent space visualizations. Moreover, we perform a utility-fairness study, and ablation and runtime experiments to illustrate the impact of the size of the reference dataset and FairShap's computational cost depending on the size of the dataset and the number of features. We believe that FairShap represents a promising direction in interpretable and model-agnostic approaches to algorithmic fairness that yield competitive accuracy even when only biased datasets are available.

翻译：算法公平性具有至关重要的社会意义，然而当前大规模机器学习模型的训练趋势依赖于通常存在偏差的海量数据集。在此背景下，聚焦于建模和校正数据偏差的预处理方法成为重要手段。本文提出FairShap，这是一种新颖的实例级数据重加权方法，通过基于沙普利值的数据估值实现公平算法决策。FairShap具有模型无关性和高度可解释性，能够衡量每个训练数据点对预定义公平性指标的贡献。我们在多个不同性质的最先进数据集上，结合多种训练场景和模型进行了实证验证，结果表明FairShap在模型准确率与基线相当的情况下，能够生成更公平的模型。我们通过直方图和潜在空间可视化展示了FairShap的可解释性。此外，我们进行了效用-公平性研究，并开展了消融实验和运行时实验，以说明参考数据集规模对FairShap的影响以及其计算成本与数据集规模和特征数量的关系。我们相信FairShap代表了算法公平性中可解释且模型无关方法的一个有前景的方向，即便仅能使用存在偏差的数据集，也能获得具有竞争力的准确率。