Cohort Shapley value is a model-free method of variable importance grounded in game theory that does not use any unobserved and potentially impossible feature combinations. We use it to evaluate algorithmic fairness, using the well known COMPAS recidivism data as our example. This approach allows one to identify for each individual in a data set the extent to which they were adversely or beneficially affected by their value of a protected attribute such as their race. The method can do this even if race was not one of the original predictors and even if it does not have access to a proprietary algorithm that has made the predictions. The grounding in game theory lets us define aggregate variable importance for a data set consistently with its per subject definitions. We can investigate variable importance for multiple quantities of interest in the fairness literature including false positive predictions.

翻译：Cohort Shapley 价值是一种无模型的、具有不同重要性的模型方法,它基于游戏理论,不使用任何未观测的和可能不可能的特性组合。我们用它来评估算法公平性,使用众所周知的COMPAS累犯数据作为我们的例子。这种方法允许一个人在一组数据中为每个人确定他们在多大程度上受到种族等受保护属性的价值的不利影响或好处。即使种族不是原始预测者之一,即使它没有机会获得作出预测的专利算法,这种方法也可以做到这一点。以游戏理论为基础,让我们确定与每个主题定义一致的一组数据的总变量重要性。我们可以调查公平文献中多种利益的不同重要性,包括错误的正面预测。