Publication
Title
Classifying without discriminating
Author
Abstract
Classification models usually make predictions on the basis of training data. If the training data is biased towards certain groups or classes of objects, e.g., there is racial discrimination towards black people, the learned model will also show discriminatory behavior towards that particular community. This partial attitude of the learned model may lead to biased outcomes when labeling future unlabeled data objects. Often, however, impartial classification results are desired or even required by lam, for future data objects in spite of having biased training data. In this paper, we tackle this problem by introducing a new classification scheme for learning unbiased models on biased training data. Our method is based on massaging the dataset by making the least intrusive modifications which lead to an unbiased dataset. On this modified dataset we then learn a non-discriminating classifier. The proposed method has been implemented and experimental results on a credit approval dataset show promising results: in all experiments our method is able to reduce the prejudicial behavior for future classification significantly without loosing too much predictive accuracy.
Language
English
Source (book)
2nd International Conference on Computer, Control and Communication, February 17-18, 2008, Karahi, Pakistan
Publication
New York, N.Y. : IEEE, 2009
ISBN
978-1-4244-3312-4
Volume/pages
(2009), p. 337-342
ISI
000267589700062
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 23.06.2016
Last edited 27.11.2017