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.
翻译:尽管最近在算法公平方面有所进展,但普遍线性模型(GLMs)实现公平的方法尚未普遍探讨,尽管在实际中广泛使用GLMs。在本文件中,我们引入了基于对预期结果或日志相似性进行平衡的两种GLMs公平标准。我们证明,对于GLM来说,这两种标准都可以通过仅仅基于GLM线性成分的直线性处罚术语实现,从而可以实现高效优化。我们还为由此形成的公平的GLM测量员提供了理论属性。要实证地展示拟议的公平GLM的功效,我们将它与其他众所周知的公平预测方法相比较,以广泛一套基准数据集作为二元分类和回归的基础。此外,我们证明,公平的GLMs可以对一系列反应变量作出公平的预测,而不是二元和连续的结果。