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.

翻译：本文提出FairShap，一种新颖且可解释的预处理（重加权）方法，通过数据估值实现公平的算法决策。FairShap基于Shapley值——博弈论中用于实现资源公平分配的一种著名数学框架。我们的方法易于解释，因为它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新数据集上，结合不同的训练场景和模型，对FairShap进行了实证验证。所提出的方法优于其他方法，能够在保持相似准确率水平的同时生成显著更公平的模型。此外，我们通过直方图和潜在空间可视化直观展示了FairShap的可解释性。我们认为，这项工作代表了算法公平性领域中可解释、模型无关方法的一个有前景的方向。