In this chapter we give three solutions for the discrimination-aware classification problem that are based upon Bayesian classifiers. These classifiers model the complete probability distribution by making strong independence assumptions. First we discuss the necessity of having discrimination-free classification for probabilistic models. Then we will show three ways to adapt a Naive Bayes classifier in order to make it discrimination-free. The first technique is based upon setting different thresholds for the different communities. The second technique will learn two different models for both communities, while the third model describes how we can incorporate our belief of how discrimination was added to the decisions in the training data as a latent variable. By explicitly modeling the discrimination, we can reverse engineer decisions. Since all three models can be seen as ways to introduce positive discrimination, we end the chapter with a reflection on positive discrimination.
|Title of host publication||Discrimination and Privacy in the Information Society: Effects of Data Mining and Profiling Large Databases|
|Editors||B.H.M. Custers, T.G.K. Calders, B.W. Schermer, T.Z. Zarsky|
|Place of Publication||Berlin|
|Publication status||Published - 2013|
|Name||Studies in Applied Philosophy, Epistemology and Rational Ethics|