The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algorithms. With these needs in mind, we propose a mixed integer optimization (MIO) framework for learning optimal classification trees -- one of the most interpretable models -- that can be augmented with arbitrary fairness constraints. In order to better quantify the "price of interpretability", we also propose a new measure of model interpretability called decision complexity that allows for comparisons across different classes of machine learning models. We benchmark our method against state-of-the-art approaches for fair classification on popular datasets; in doing so, we conduct one of the first comprehensive analyses of the trade-offs between interpretability, fairness, and predictive accuracy. Given a fixed disparity threshold, our method has a price of interpretability of about 4.2 percentage points in terms of out-of-sample accuracy compared to the best performing, complex models. However, our method consistently finds decisions with almost full parity, while other methods rarely do.
翻译:在高取量领域(人们的生计受到影响)越来越多地使用机器学习,这产生了对可解释、公平和高度准确的算法的迫切需要。考虑到这些需要,我们提议一个混合整数优化框架,用于学习最佳分类树(最容易解释的模式之一),这种框架可以随任意的公平性限制而增加。为了更好地量化“可解释性价格”,我们还提议了一个新的可解释性模型衡量标准,称为决定复杂性,可以对不同种类的机器学习模式进行比较。我们用最先进的方法来衡量我们如何对流行数据集进行公平分类;我们这样做是为了对可解释性、公平性和预测性准确性之间的权衡进行第一次全面分析。考虑到固定的差异门槛,我们的方法与最佳、复杂模型相比,在超现精确度方面大约4.2个百分点的可解释性价格。然而,我们的方法始终以几乎完全对等的方式作出决定,而其他方法则很少做到。