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：在本文中,我们提出FairShap,这是通过数据估价进行公平算法决策的一种新颖和可解释的预处理(再加权)方法。FairShap以Shapley value为基础,这是一个众所周知的数学框架,它来自游戏理论,从游戏理论到公平分配资源。我们的方法很容易解释,因为它衡量了每项培训数据对预先界定的公平度指标的贡献。我们从经验上验证了不同性质、具有不同培训情景和模型的若干最先进的数据集的FairShap。拟议方法优于其他方法,产生了类似精确度的更公平的模型。此外,我们用直方图和潜在的空间可视化来说明FairShap的解释性。我们认为,这项工作代表了对算法公平采取可解释的、模型性、不可知性方法的一个很有希望的方向。</s>