Techniques for Discrimination-Free Predictive Models - Discrimination and Privacy in the Information Society - page 214

Chapter 12
Techniques for Discrimination-Free Predictive
Models
Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy
Abstract. In this chapter, we give an overview of the techniques developed our-
selves for constructing discrimination-free classifiers. In discrimination-free classi-
fication 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 dis-
criminatory 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 tech-
niques succeed in significantly reducing gender discrimination without impairing
too much the accuracy.