In recent years, machine learning has begun automating decision making in fields as varied as college admissions, credit lending, and criminal sentencing. The socially sensitive nature of some of these applications together with increasing regulatory constraints has necessitated the need for algorithms that are both fair and interpretable. In this paper we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on two different measures of classification parity: equality of opportunity and equalized odds. Column generation framework, with a novel formulation, is used to efficiently search over exponentially many possible rules. When combined with faster heuristics, our method can deal with large data-sets. Compared to other fair and interpretable classifiers, our method is able to find rule sets that meet stricter notions of fairness with a modest trade-off in accuracy.
翻译:近年来,机器学习开始在大学入学、信贷贷款和刑事判决等不同领域实现决策自动化,其中一些应用的社会敏感性,加上越来越多的监管限制,使得有必要采用公平和可解释的算法。在本文中,我们认为建立布尔兰规则的问题是一个分流的正常形式(DNF)的问题,这是可解释的二进制分类模式,但受公平限制。我们将此问题编成一个整数程序,最大限度地实现分类准确性,明确限制两种不同的分类均等衡量标准:机会平等和机会均等。用一种新颖的公式生成专栏生成框架,用于快速地搜索许多可能的规则。当我们的方法与较快的超自然规则相结合时,我们的方法可以处理大量的数据集。与其他公平和可解释的分类者相比,我们的方法可以找到更严格、更准确的公平概念。