Introducing Positive Discrimination in Predictive Models

Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 3)

Abstract

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.

Keywords

Class Label Decision Threshold Latent Variable Model Positive Class Sensitive Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)Google Scholar
  2. Calders, T., Kamiran, F., Pechenizkiy, M.: Building classifiers with independency constraints. In: IEEE ICDM Workshop on Domain Driven Data Mining, pp. 13–18. IEEE press (2009)Google Scholar
  3. Calders, T., Verwer, S.: Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery 21(2), 277–292 (2010)MathSciNetCrossRefGoogle Scholar
  4. Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., Weingessel, A.: e1071: Misc functions of the Department of Statistics. TU Wien, R package version 1 (2008)Google Scholar
  5. Kamiran, F., Calders, T.: Classifying without discriminating. In: Proc. IEEE International Conference on Computer, Control and Communication (IC4), pp. 1–6. IEEE press (2009)Google Scholar
  6. Lachiche, N., Flach, P.: Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In: Proc. International Conference on Machine Learning (ICML), pp. 416–423. AAAI Press (2003)Google Scholar
  7. Langley, P., Iba, W., Thompson, K.: An analysis of Bayesian classifiers. In: Proc. Conference on Artificial Intelligence (AAAI), pp. 223–228 (1992)Google Scholar
  8. Pedreschi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 560–568 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Research and Documentation Centre(WODC) of the Ministry of Security and JusticeAmsterdamThe Netherlands
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

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