In past work on fairness in machine learning, the focus has been on forcing the prediction of classifiers to have similar statistical properties for people of different demographics. To reduce the violation of these properties, fairness methods usually simply rescale the classifier scores, ignoring similarities and dissimilarities between members of different groups. Yet, we hypothesize that such information is relevant in quantifying the unfairness of a given classifier. To validate this hypothesis, we introduce Optimal Transport to Fairness (OTF), a method that quantifies the violation of fairness constraints as the smallest Optimal Transport cost between a probabilistic classifier and any score function that satisfies these constraints. For a flexible class of linear fairness constraints, we construct a practical way to compute OTF as a differentiable fairness regularizer that can be added to any standard classification setting. Experiments show that OTF can be used to achieve an improved trade-off between predictive power and fairness.
翻译:在以往关于机器学习公平性的工作中,重点一直是迫使分类者预测对不同人口群体的人具有类似的统计特性。为了减少对这些特性的违反,公平方法通常只是调整分类者的分数,忽视不同群体成员之间的异同。然而,我们假设此类信息在量化特定分类者的不公平性方面具有相关性。为了证实这一假设,我们引入了最佳运输到公平性(OTF),这种方法将违反公平性限制的情况量化为概率性分类者与满足这些限制的任何得分函数之间的最小最佳运输成本。对于灵活的线性公平性限制类别,我们设计了一种实际方法,将OTF计算成一种不同的公平性规范,可以添加到任何标准的分类设置中。实验表明,OTF可以用来改进预测力与公平之间的交易。