In this paper, we propose FairShap, a novel and interpretable pre-processing
(re-weighting) method for fair algorithmic decision-making through data
valuation. FairShap is based on the Shapley Value, a well-known mathematical
framework from game theory to achieve a fair allocation of resources. Our
approach is 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
different training scenarios and models. The proposed approach outperforms
other methods, yielding significantly fairer models with similar levels of
accuracy. In addition, we illustrate FairShap's interpretability by means of
histograms and latent space visualizations. We believe this work represents a
promising direction in interpretable, model-agnostic approaches to algorithmic
fairness.

本文提出了一种新颖且可解释的预处理方法 FairShap，用于通过数据估值实现公平的算法决策。FairShap 基于 Shapley Value 框架，并通过测量每个训练数据点对预定义公平度量的贡献来实现可解释性。我们在多个不同性质、不同训练场景和模型的最新数据集上进行了实证验证，该方法在表现上优于其他方法，并产生了具有类似精度水平的显着更公平的模型。此外，我们通过直方图和潜在空间可视化说明了 FairShap 的可解释性，我们相信这项工作代表了算法公平性可解释、深度学习可解释性等面向的有前途的方向。