The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable and fair algorithms. In these settings it is also critical for such algorithms to be accurate. With these needs in mind, we propose a mixed integer optimization (MIO) framework for learning optimal classification trees of fixed depth that can be conveniently augmented with arbitrary domain specific fairness constraints. We benchmark our method against the state-of-the-art approach for building fair trees on popular datasets; given a fixed discrimination threshold, our approach improves out-of-sample (OOS) accuracy by 2.3 percentage points on average and obtains a higher OOS accuracy on 88.9% of the experiments. We also incorporate various algorithmic fairness notions into our method, showcasing its versatile modeling power that allows decision makers to fine-tune the trade-off between accuracy and fairness.

翻译：在人们的生计受到影响的高风险领域越来越多地使用机器学习,这产生了对可解释和公平算法的迫切需要。在这些环境中,这种算法也必须准确。考虑到这些需要,我们提议一个混合整数优化框架,以学习固定深度的最佳分类树,这种分类可以方便地增加,并带有任意的地域具体的公平限制。我们参照最先进的方法,在流行数据集的基础上建造公平树;考虑到固定的歧视阈值,我们的方法使标本(OOS)的准确性平均提高2.3个百分点,并使88.9%的实验得到更高的OOS准确性。我们还将各种算法公平概念纳入我们的方法,展示其多功能模型,使决策者能够调整准确和公平之间的交易。