论文标题
公平的离群检测
Fair Outlier Detection
论文作者
论文摘要
如果在此敏感属性上定义的特定组，则可以将异常检测方法视为指定敏感属性的公平。在这项任务中，我们首次考虑公平的离群检测任务。在这项工作中，我们考虑了多个多价敏感属性（例如性别，种族，宗教，国籍，婚姻状况等）的公平离群检测任务。我们提出了一种公平的离群检测方法Fairlof，其灵感来自于流行的基于邻里的离群值检测的LOF公式。我们概述了在LOF中诱发不公平的方式并制定了三种启发式原则以增强公平性，这构成了Fairlof方法的基础。作为一项新颖的任务，我们开发了一个评估框架，以进行公平的离群检测，并将其用于基准成果的质量和公平性。通过对现实世界数据集进行广泛的经验评估，我们说明Fairlof能够在有时在边缘降解的结果质量上，以实现公平性 - 敏捷的LOF方法来取得显着改善。
An outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed towards particular groups defined on such sensitive attributes. In this task, we consider, for the first time to our best knowledge, the task of fair outlier detection. In this work, we consider the task of fair outlier detection over multiple multi-valued sensitive attributes (e.g., gender, race, religion, nationality, marital status etc.). We propose a fair outlier detection method, FairLOF, that is inspired by the popular LOF formulation for neighborhood-based outlier detection. We outline ways in which unfairness could be induced within LOF and develop three heuristic principles to enhance fairness, which form the basis of the FairLOF method. Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmark FairLOF on quality and fairness of results. Through an extensive empirical evaluation over real-world datasets, we illustrate that FairLOF is able to achieve significant improvements in fairness at sometimes marginal degradations on result quality as measured against the fairness-agnostic LOF method.
