Title: Discrimination-aware data mining: a survey

Authors: Asmita Kashid; Vrushali Kulkarni; Ruhi Patankar

Addresses: Department of Computer Engineering, Maharashtra Institute of Technology, Savitribai Phule Pune University, Maharashtra 411038, India ' Department of Computer Engineering, Maharashtra Institute of Technology, Savitribai Phule Pune University, Maharashtra 411038, India ' Department of Computer Engineering, Maharashtra Institute of Technology, Savitribai Phule Pune University, Maharashtra 411038, India

Abstract: Data mining is a very important and useful technique to extract knowledge from raw data. However, there is a challenge faced by data mining researchers, in the form of potential discrimination. Discrimination means giving unfair treatment to a person just because one belongs to a minority group, without considering one's individual merit or qualification. The results extracted using data mining techniques may lead to discrimination, if a biased historical/training dataset is used. It is very important to prevent data mining technique from becoming a source of discrimination. A detailed survey of discrimination discovery methods and discrimination prevention methods is presented in this paper. This paper also presents the list of datasets used for experiments in different discrimination-aware data mining (DADM) approaches. Some ideas for future research work that may help in preventing discrimination are also discussed.

Keywords: DADM; discrimination-aware data mining; discrimination discovery; discrimination prevention; biased datasets; bias.

DOI: 10.1504/IJDS.2017.082748

International Journal of Data Science, 2017 Vol.2 No.1, pp.70 - 84

Received: 10 Sep 2014
Accepted: 28 Mar 2015
Published online: 10 Mar 2017 *

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