The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent. While regulatory frameworks, including the EU's AI Act, mandate bias mitigation, they are deliberately vague about the definition of bias. In line with existing research, we argue that true fairness requires addressing bias at the intersections of protected groups. We propose a unified framework that leverages Mixed-Integer Optimization (MIO) to train intersectionally fair and intrinsically interpretable classifiers. We prove the equivalence of two measures of intersectional fairness (MSD and SPSF) in detecting the most unfair subgroup and empirically demonstrate that our MIO-based algorithm improves performance in finding bias. We train high-performing, interpretable classifiers that bound intersectional bias below an acceptable threshold, offering a robust solution for regulated industries and beyond.

翻译：人工智能在金融和医疗等高风险领域的应用，要求模型兼具公平性与透明度。尽管包括欧盟《人工智能法案》在内的监管框架要求缓解偏见，但这些框架对偏见的定义却刻意保持模糊。与现有研究一致，我们认为真正的公平需要在受保护群体的交叉点上解决偏见问题。我们提出了一个统一框架，该框架利用混合整数优化（MIO）来训练具有交叉公平性且本质可解释的分类器。我们证明了两种交叉公平性度量（MSD和SPSF）在检测最不公平子群方面的等价性，并通过实验验证了我们基于MIO的算法在发现偏见方面提升了性能。我们训练了高性能、可解释的分类器，这些分类器能将交叉偏见限制在可接受的阈值以下，为受监管行业及其他领域提供了一个稳健的解决方案。