To study discrimination in automated decision-making systems, scholars have proposed several definitions of fairness, each expressing a different fair ideal. These definitions require practitioners to make complex decisions regarding which notion to employ and are often difficult to use in practice since they make a binary judgement a system is fair or unfair instead of explaining the structure of the detected unfairness. We present an optimal transport-based approach to fairness that offers an interpretable and quantifiable exploration of bias and its structure by comparing a pair of outcomes to one another. In this work, we use the optimal transport map to examine individual, subgroup, and group fairness. Our framework is able to recover well known examples of algorithmic discrimination, detect unfairness when other metrics fail, and explore recourse opportunities.

中文翻译：

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一切都是相对的：通过最佳运输了解公平

为了研究自动决策系统中的歧视，学者们提出了几种公平的定义，每种定义都表达了不同的公平理想。这些定义要求从业人员就采用哪种概念做出复杂的决定，并且通常很难在实践中使用，因为他们会做出二元判断，即系统是公平的还是不公平的，而不是解释所检测到的不公平的结构。我们提出了一种基于运输的最佳公平方法，通过将一对结果相互比较，可以对偏差及其结构进行可解释和量化的探索。在这项工作中，我们使用最佳运输图来检查个人，亚组和组的公平性。我们的框架能够恢复众所周知的算法歧视示例，在其他指标失败时检测不公平现象，