Title
Classifying without discriminating Classifying without discriminating
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
New York, N.Y. :IEEE, [*]
Subject
Mass communications
Computer. Automation
Source (book)
2nd International Conference on Computer, Control and Communication, February 17-18, 2008, Karahi, Pakistan
ISBN
978-1-4244-3312-4
ISI
000267589700062
Carrier
E
Target language
English (eng)
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.
E-info
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