Decision theory for discrimination-aware classification

Handle URI:
http://hdl.handle.net/10754/564630
Title:
Decision theory for discrimination-aware classification
Authors:
Kamiran, Faisal; Karim, Asim A.; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
Social discrimination (e.g., against females) arising from data mining techniques is a growing concern worldwide. In recent years, several methods have been proposed for making classifiers learned over discriminatory data discriminationaware. However, these methods suffer from two major shortcomings: (1) They require either modifying the discriminatory data or tweaking a specific classification algorithm and (2) They are not flexible w.r.t. discrimination control and multiple sensitive attribute handling. In this paper, we present two solutions for discrimination-aware classification that neither require data modification nor classifier tweaking. Our first and second solutions exploit, respectively, the reject option of probabilistic classifier(s) and the disagreement region of general classifier ensembles to reduce discrimination. We relate both solutions with decision theory for better understanding of the process. Our experiments using real-world datasets demonstrate that our solutions outperform existing state-ofthe-art methods, especially at low discrimination which is a significant advantage. The superior performance coupled with flexible control over discrimination and easy applicability to multiple sensitive attributes makes our solutions an important step forward in practical discrimination-aware classification. © 2012 IEEE.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Machine Intelligence & kNowledge Engineering Lab
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2012 IEEE 12th International Conference on Data Mining
Conference/Event name:
12th IEEE International Conference on Data Mining, ICDM 2012
Issue Date:
Dec-2012
DOI:
10.1109/ICDM.2012.45
Type:
Conference Paper
ISSN:
15504786
ISBN:
9780769549057
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKamiran, Faisalen
dc.contributor.authorKarim, Asim A.en
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2015-08-04T07:05:32Zen
dc.date.available2015-08-04T07:05:32Zen
dc.date.issued2012-12en
dc.identifier.isbn9780769549057en
dc.identifier.issn15504786en
dc.identifier.doi10.1109/ICDM.2012.45en
dc.identifier.urihttp://hdl.handle.net/10754/564630en
dc.description.abstractSocial discrimination (e.g., against females) arising from data mining techniques is a growing concern worldwide. In recent years, several methods have been proposed for making classifiers learned over discriminatory data discriminationaware. However, these methods suffer from two major shortcomings: (1) They require either modifying the discriminatory data or tweaking a specific classification algorithm and (2) They are not flexible w.r.t. discrimination control and multiple sensitive attribute handling. In this paper, we present two solutions for discrimination-aware classification that neither require data modification nor classifier tweaking. Our first and second solutions exploit, respectively, the reject option of probabilistic classifier(s) and the disagreement region of general classifier ensembles to reduce discrimination. We relate both solutions with decision theory for better understanding of the process. Our experiments using real-world datasets demonstrate that our solutions outperform existing state-ofthe-art methods, especially at low discrimination which is a significant advantage. The superior performance coupled with flexible control over discrimination and easy applicability to multiple sensitive attributes makes our solutions an important step forward in practical discrimination-aware classification. © 2012 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleDecision theory for discrimination-aware classificationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Laben
dc.identifier.journal2012 IEEE 12th International Conference on Data Miningen
dc.conference.date10 December 2012 through 13 December 2012en
dc.conference.name12th IEEE International Conference on Data Mining, ICDM 2012en
dc.conference.locationBrusselsen
dc.contributor.institutionLahore University of Management Sciences, Pakistanen
kaust.authorZhang, Xiangliangen
kaust.authorKamiran, Faisalen
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