TY - JOUR
T1 - Exploiting Reject Option in Classification for Social Discrimination Control
AU - Kamiran, Faisal
AU - Mansha, Sameen
AU - Karim, Asim
AU - Zhang, Xiangliang
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2017/9/29
Y1 - 2017/9/29
N2 - Social 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.
AB - Social 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.
UR - http://hdl.handle.net/10754/625533
UR - http://www.sciencedirect.com/science/article/pii/S0020025517309830
UR - http://www.scopus.com/inward/record.url?scp=85032376541&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2017.09.064
DO - 10.1016/j.ins.2017.09.064
M3 - Article
SN - 0020-0255
VL - 425
SP - 18
EP - 33
JO - Information Sciences
JF - Information Sciences
ER -