论文标题
事实：团体公平权衡的诊断
FACT: A Diagnostic for Group Fairness Trade-offs
论文作者
论文摘要
群体公平是一类公平的概念，这些概念衡量了如何根据受保护的属性对不同的个体进行不同的对待，但已被证明是彼此冲突的，通常在失去模型的预测性能方面具有必要的成本。我们提出了一个一般诊断，可以在群体公平性中系统地表征这些权衡。我们观察到，大多数群体公平概念可以通过公平灌注量张量表示，这是根据受保护的属性值的混淆矩阵拆分。我们构建了几个优化问题，这些问题直接优化了该张量的元素的准确性和公平目标，这对理解包括集体公平不兼容在内的多个权衡的一般观点产生了一般的看法。它还提出了一种设计公平分类器的替代后处理方法。在合成和真实数据集上，我们演示了诊断的用例，尤其是在了解准确性和公平性之间的权衡景观方面。
Group fairness, a class of fairness notions that measure how different groups of individuals are treated differently according to their protected attributes, has been shown to conflict with one another, often with a necessary cost in loss of model's predictive performance. We propose a general diagnostic that enables systematic characterization of these trade-offs in group fairness. We observe that the majority of group fairness notions can be expressed via the fairness-confusion tensor, which is the confusion matrix split according to the protected attribute values. We frame several optimization problems that directly optimize both accuracy and fairness objectives over the elements of this tensor, which yield a general perspective for understanding multiple trade-offs including group fairness incompatibilities. It also suggests an alternate post-processing method for designing fair classifiers. On synthetic and real datasets, we demonstrate the use cases of our diagnostic, particularly on understanding the trade-off landscape between accuracy and fairness.
