Algorithmic Fairness and the explainability of potentially unfair outcomes are crucial for establishing trust and accountability of Artificial Intelligence systems in domains such as healthcare and policing. Though significant advances have been made in each of the fields separately, achieving explainability in fairness applications remains challenging, particularly so in domains where deep neural networks are used. At the same time, ethical data-mining has become ever more relevant, as it has been shown countless times that fairness-unaware algorithms result in biased outcomes. Current approaches focus on mitigating biases in the outcomes of the model, but few attempts have been made to try to explain \emph{why} a model is biased. To bridge this gap, we propose a comprehensive approach that leverages optimal transport theory to uncover the causes and implications of biased regions in images, which easily extends to tabular data as well. Through the use of Wasserstein barycenters, we obtain scores that are independent of a sensitive variable but keep their marginal orderings. This step ensures predictive accuracy but also helps us to recover the regions most associated with the generation of the biases. Our findings hold significant implications for the development of trustworthy and unbiased AI systems, fostering transparency, accountability, and fairness in critical decision-making scenarios across diverse domains.

算法公平性和不公平结果的可解释性对于在医疗保健和执法等领域建立人工智能系统的信任和问责是至关重要的。我们提出了一种基于最优运输理论的全面方法，通过发现图像中有偏差区域的原因和影响来建立模型的解释性。这些发现对于开发值得信赖、公正的AI系统，在各个领域的决策中提供透明、问责和公平具有重要意义。

利用最优传输解决图像分类中的公平性和可解释性问题