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Review Paper on Prevention of Direct and Indirect Discrimination

Published on December 2014 by Trupti N. Mahale, Amol D. Potgantawar
Innovations and Trends in Computer and Communication Engineering
Foundation of Computer Science USA
ITCCE - Number 4
December 2014
Authors: Trupti N. Mahale, Amol D. Potgantawar
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Trupti N. Mahale, Amol D. Potgantawar . Review Paper on Prevention of Direct and Indirect Discrimination. Innovations and Trends in Computer and Communication Engineering. ITCCE, 4 (December 2014), 12-15.

@article{
author = { Trupti N. Mahale, Amol D. Potgantawar },
title = { Review Paper on Prevention of Direct and Indirect Discrimination },
journal = { Innovations and Trends in Computer and Communication Engineering },
issue_date = { December 2014 },
volume = { ITCCE },
number = { 4 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 12-15 },
numpages = 4,
url = { /proceedings/itcce/number4/19061-2027/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Innovations and Trends in Computer and Communication Engineering
%A Trupti N. Mahale
%A Amol D. Potgantawar
%T Review Paper on Prevention of Direct and Indirect Discrimination
%J Innovations and Trends in Computer and Communication Engineering
%@ 0975-8887
%V ITCCE
%N 4
%P 12-15
%D 2014
%I International Journal of Computer Applications
Abstract

Data mining is very important technology for extracting useful knowledge from large data. The discrimination is nothing but the unfair treatment given to an individual or group according to particular characteristics. For data mining classification rules are performing very important role but discrimination comes into picture because of biased classification rules. The training data sets are biased so we need to firstly discover discrimination and then need to prevent that discrimination to make it discrimination free. Discrimination can be of two types, direct and indirect. When decisions are made based on sensitive attributes, Direct Discrimination occurs. While decisions based on non-sensitive attributes, Indirect Discrimination occurs. The experimental evaluations demonstrate that the proposed techniques are effective at removing direct and/or indirect discrimination in the original data set while preserving data quality.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Rule Protection Rule Generalization Antidiscrimination