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On Discrimination Discovery Using Causal Networks

  • Lu ZhangEmail author
  • Yongkai Wu
  • Xintao Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)

Abstract

Discrimination discovery is an increasingly important task in the data mining field. The purpose of discrimination discovery is to unveil discriminatory practices on the protective attribute (e.g., gender) by analyzing the dataset of historical decision records. Different types of discrimination have been proposed in the literature. We aim to develop a framework that is able to deal with all types of discrimination. We make use of the causal networks, which effectively captures the existence of discrimination patterns and can provide quantitative evidence of discrimination in decision making. In this paper, we first propose a categorization for various discrimination. Then, we present our preliminary results on four types of discrimination, namely system-level direct discrimination, the system-level indirect discrimination, group-level discrimination, and individual level discrimination. We have conducted empirical assessments on real datasets. The results show great efficacy of our approach.

Notes

Acknowledgment

This work was supported in part by U.S. National Institute of Health (1R01GM103309).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of ArkansasFayettevilleUSA

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