FairLOF: Fairness in Outlier Detection
P Deepak, Savitha Sam Abraham · Data Science and Engineering · 2021
Abstract An outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed toward particular groups defined on such sensitive attributes. In this paper, we consider the task of fair outlier detection. Our focus is on the task of fair outlier detection over multiple multi-valued sensitive attributes (e.g., gender, race, religion, nationality and marital status, among others), one that has broad applications across modern data scenarios. We propose a fair outlier detection method,FairLOF, that is inspired by the popularLOFformulation for neighborhood-based outlier detection. We outline ways in which unfairness could be induced withinLOFand develop three heuristic principles to enhance fairness, which form the basis of theFairLOFmethod. Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmarkFairLOFon quality and fairness of results. Through an extensive empirical evaluation over real-world datasets, we illustrate thatFairLOFis able to achieve significant improvements in fairness at sometimes marginal degradations on result quality as measured against the fairness-agnosticLOFmethod. We also show that a generalization of our method, namedFairLOF-Flex, is able to open possibilities of further deepening fairness in outlier detection beyond what is offered byFairLOF.