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dc.contributor.authorKamiran, Faisal
dc.contributor.authorMansha, Sameen
dc.contributor.authorKarim, Asim
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2017-10-02T10:53:16Z
dc.date.available2017-10-02T10:53:16Z
dc.date.issued2017-09-29
dc.identifier.citationKamiran F, Mansha S, Karim A, Zhang X (2017) Exploiting Reject Option in Classification for Social Discrimination Control. Information Sciences. Available: http://dx.doi.org/10.1016/j.ins.2017.09.064.
dc.identifier.issn0020-0255
dc.identifier.doi10.1016/j.ins.2017.09.064
dc.identifier.urihttp://hdl.handle.net/10754/625533
dc.description.abstractSocial discrimination is said to occur when an unfavorable decision for an individual is influenced by her membership to certain protected groups such as females and minority ethnic groups. Such discriminatory decisions often exist in historical data. Despite recent works in discrimination-aware data mining, there remains the need for robust, yet easily usable, methods for discrimination control. In this paper, we utilize reject option in classification, a general decision theoretic framework for handling instances whose labels are uncertain, for modeling and controlling discriminatory decisions. Specifically, this framework permits a formal treatment of the intuition that instances close to the decision boundary are more likely to be discriminated in a dataset. Based on this framework, we present three different solutions for discrimination-aware classification. The first solution invokes probabilistic rejection in single or multiple probabilistic classifiers while the second solution relies upon ensemble rejection in classifier ensembles. The third solution integrates one of the first two solutions with situation testing which is a procedure commonly used in the court of law. All solutions are easy to use and provide strong justifications for the decisions. We evaluate our solutions extensively on four real-world datasets and compare their performances with previously proposed discrimination-aware classifiers. The results demonstrate the superiority of our solutions in terms of both performance and flexibility of applicability. In particular, our solutions are effective at removing illegal discrimination from the predictions.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0020025517309830
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, [, , (2017-09-29)] DOI: 10.1016/j.ins.2017.09.064 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDiscrimination-aware Data Mining
dc.subjectFairness in Machine Learning
dc.subjectClassification
dc.subjectDecision Theory
dc.titleExploiting Reject Option in Classification for Social Discrimination Control
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalInformation Sciences
dc.eprint.versionPost-print
dc.contributor.institutionInformation Technology University, Lahore, Pakistan
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
dc.contributor.institutionDepartment of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
kaust.personZhang, Xiangliang
refterms.dateFOA2019-09-29T00:00:00Z


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