论文标题
公平通用的线性模型，具有凸惩罚
Fair Generalized Linear Models with a Convex Penalty
论文作者
论文摘要
尽管算法公平最近取得了进步，但通过广义线性模型（GLM）实现公平性的方法，尽管GLM在实践中被广泛使用，但尚待探索。在本文中，我们基于均等的预期结果或对数类样的介绍了两个公平性GLMS的标准。我们证明，对于GLMS，这两个标准都可以仅基于GLM的线性组件的凸惩罚项来实现，从而允许有效优化。我们还得出了由此产生的公平GLM估计器的理论特性。为了从经验上证明所提出的公平GLM的疗效，我们将其与其他众所周知的公平预测方法进行了比较，以用于二进制分类和回归的广泛基准数据集。此外，我们证明了公平的GLM可以为二进制和连续结果以外的一系列响应变量产生公平的预测。
Despite recent advances in algorithmic fairness, methodologies for achieving fairness with generalized linear models (GLMs) have yet to be explored in general, despite GLMs being widely used in practice. In this paper we introduce two fairness criteria for GLMs based on equalizing expected outcomes or log-likelihoods. We prove that for GLMs both criteria can be achieved via a convex penalty term based solely on the linear components of the GLM, thus permitting efficient optimization. We also derive theoretical properties for the resulting fair GLM estimator. To empirically demonstrate the efficacy of the proposed fair GLM, we compare it with other well-known fair prediction methods on an extensive set of benchmark datasets for binary classification and regression. In addition, we demonstrate that the fair GLM can generate fair predictions for a range of response variables, other than binary and continuous outcomes.
