City Research Online

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.

Publication Type:	Article
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher Keywords:	Outlier detection, Fairness, Unsupervised learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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