Int. J. of Data Science   »   2017 Vol.2, No.1

 

 

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

 

Int. J. of Data Science, 2017 Vol.2, No.1, pp.70 - 84

 

Available online: 10 Mar 2017

 

 

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