Techniques for discrimination-free predictive models

F. Kamiran, T.G.K. Calders, M. Pechenizkiy

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

11 Citations (Scopus)
4 Downloads (Pure)


In this chapter, we give an overview of the techniques developed ourselves for constructing discrimination-free classifiers. In discrimination-free classification the goal is to learn a predictive model that classifies future data objects as accurately as possible, yet the predicted labels should be uncorrelated to a given sensitive attribute. For example, the task could be to learn a gender-neutral model that predicts whether a potential client of a bank has a high income or not. The techniques we developed for discrimination-aware classification can be divided into three categories: (1) removing the discrimination directly from the historical dataset before an off-the-shelf classification technique is applied; (2) changing the learning procedures themselves by restricting the search space to non-discriminatory models; and (3) adjusting the discriminatory models, learnt by off-the-shelf classifiers on discriminatory historical data, in a post-processing phase. Experiments show that even with such a strong constraint as discrimination-freeness, still very accurate models can be learnt. In particular,we study a case of income prediction,where the available historical data exhibits a wage gap between the genders. Due to legal restrictions, however, our predictions should be gender-neutral. The discrimination-aware techniques succeed in significantly reducing gender discrimination without impairing too much the accuracy.
Original languageEnglish
Title of host publicationDiscrimination and Privacy in the Information Society: Effects of Data Mining and Profiling Large Databases
EditorsB.H.M. Custers, T.G.K. Calders, B.W. Schermer, T.Z. Zarsky
Place of PublicationBerlin
ISBN (Print)978-3-642-30486-6
Publication statusPublished - 2013

Publication series

NameStudies in Applied Philosophy, Epistemology and Rational Ethics
ISSN (Print)2192-6255


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