论文标题
审查机器学习中公平性的数学框架
Review of Mathematical frameworks for Fairness in Machine Learning
论文作者
论文摘要
从数学的角度提出了对文献中文献中提出的主要公平定义和公平学习方法的回顾。遵循我们基于独立的方法，我们考虑如何构建公平算法以及与可能不公平的情况相比，对其性能降解的后果。这对应于标准给出的公平性的价格$ \ textit {统计奇偶} $或$ \ textit {of gedds} $。出现了新颖的结果，从而介绍了$ \ textIt {quassian {quassian模型的最佳公平分类器和最佳公平预测指标的表达方式（在线性回归的高斯模型下）。
A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view. Following our independence-based approach, we consider how to build fair algorithms and the consequences on the degradation of their performance compared to the possibly unfair case. This corresponds to the price for fairness given by the criteria $\textit{statistical parity}$ or $\textit{equality of odds}$. Novel results giving the expressions of the optimal fair classifier and the optimal fair predictor (under a linear regression gaussian model) in the sense of $\textit{equality of odds}$ are presented.
