论文标题
广义熵的公平经验风险最小化
A Fair Empirical Risk Minimization with Generalized Entropy
论文作者
论文摘要
本文研究了一个算法公平指标的参数家族，称为广义熵，该家族最初用于公共福利，最近被引入机器学习社区。作为评估算法公平性的有意义的指标，它要求广义熵指定分类问题的公平要求和公平要求，应通过算法小偏差来实现。我们通过公平的经验风险最小化将广义熵作为公平分类算法的设计参数的作用，并根据广义熵指定的约束。我们从理论上和实验研究了问题的可学习性。
This paper studies a parametric family of algorithmic fairness metrics, called generalized entropy, which originally has been used in public welfare and recently introduced to machine learning community. As a meaningful metric to evaluate algorithmic fairness, it requires that generalized entropy specify fairness requirements of a classification problem and the fairness requirements should be realized with small deviation by an algorithm. We investigate the role of generalized entropy as a design parameter for fair classification algorithm through a fair empirical risk minimization with a constraint specified in terms of generalized entropy. We theoretically and experimentally study learnability of the problem.
