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On Discrimination Discovery Using Causal Networks

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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References

  1. Bonchi, F., Hajian, S., Mishra, B., Ramazzotti, D.: Exposing the probabilistic causal structure of discrimination. arXiv preprint (2015). arXiv:1510.00552

  2. Calders, T., Verwer, S.: Three naive bayes approaches for discrimination-free classification. Data Min. Knowl. Discov. 21(2), 277–292 (2010)

    Article  MathSciNet  Google Scholar 

  3. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214–226. ACM (2012)

    Google Scholar 

  4. Glymour, C., et al.: The TETRAD project (2004). http://www.phil.cmu.edu/tetrad

  5. Hajian, S., Domingo-Ferrer, J.: A methodology for direct and indirect discrimination prevention in data mining. TKDE 25(7), 1445–1459 (2013)

    Google Scholar 

  6. Hajian, S., Domingo-Ferrer, J., Monreale, A., Pedreschi, D., Giannotti, F.: Discrimination-and privacy-aware patterns. Data Min. Knowl. Discov. 29(6), 1–50 (2014)

    MathSciNet  Google Scholar 

  7. Kamiran, F., Calders, T.: Classifying without discriminating. In: International Conference on Computer, Control and Communication, pp. 1–6 (2009)

    Google Scholar 

  8. Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1–33 (2012)

    Article  Google Scholar 

  9. Kamishima, T., Akaho, S., Sakuma, J.: Fairness-aware learning through regularization approach. In: ICDMW, pp. 643–650. IEEE (2011)

    Google Scholar 

  10. Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  11. Luong, B.T., Ruggieri, S., Turini, F.: K-nn as an implementation of situation testing for discrimination discovery and prevention. In: KDD, pp. 502–510. ACM (2011)

    Google Scholar 

  12. Mancuhan, K., Clifton, C.: Combating discrimination using bayesian networks. Artif. Intell. Law 22(2), 211–238 (2014)

    Article  Google Scholar 

  13. Statistics Netherlands. Volkstelling (2001). https://sites.google.com/site/faisalkamiran/

  14. Pearl, J.: Comment: understanding simpsons paradox. Am. Stat. 68(1), 8–13 (2014)

    Article  MathSciNet  Google Scholar 

  15. Pearl, J., et al.: Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  16. Pedreschi, D., Ruggieri, S., Turini, F.: Measuring discrimination in socially-sensitive decision records. In: SIAM SDM (2009)

    Google Scholar 

  17. Pedreshi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: KDD, pp. 560–568. ACM (2008)

    Google Scholar 

  18. Romei, A., Ruggieri, S.: A multidisciplinary survey on discrimination analysis. Knowl. Eng. Rev. 29(05), 582–638 (2014)

    Article  Google Scholar 

  19. Ruggieri, S., Hajian, S., Kamiran, F., Zhang, X.: Anti-discrimination analysis using privacy attack strategies. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014, Part II. LNCS, vol. 8725, pp. 694–710. Springer, Heidelberg (2014)

    Google Scholar 

  20. Ruggieri, S., Pedreschi, D., Turini, F.: Data mining for discrimination discovery. ACM TKDD 4(2), 9 (2010)

    Google Scholar 

  21. Spirtes, P., Glymour, C.N., Scheines, R.: Causation, Prediction, and Search, vol. 81. MIT press, Cambridge (2000)

    MATH  Google Scholar 

  22. Wu, Y., Wu, X.: Using loglinear model for discrimination discovery and prevention. Technical report, DPL-2015-002, University of Arkansas (2015)

    Google Scholar 

  23. Zhang, L., Wu, Y., Wu, X.: Causal Bayeisan network-based discrimination discovery and removal. Technical report, DPL-2016-001, University of Arkansas (2016)

    Google Scholar 

  24. Zhang, L., Yongkai, W., Xintao, W., Discovery, situation testing-based discrimination : a causal inference approach. Technical report, DPL-2016-002, University of Arkansas (2016)

    Google Scholar 

  25. Zliobaite, I., Kamiran, F., Calders, T.: Handling conditional discrimination. In: ICDM 2011, pp. 992–1001. IEEE (2011)

    Google Scholar 

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Acknowledgment

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

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Authors and Affiliations

  1. University of Arkansas, Fayetteville, USA

    Lu Zhang, Yongkai Wu & Xintao Wu

Authors
  1. Lu Zhang
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  2. Yongkai Wu
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  3. Xintao Wu
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Corresponding author

Correspondence to Lu Zhang .

Editor information

Editors and Affiliations

  1. University of Toledo, Toledo, Ohio, USA

    Kevin S. Xu

  2. Penn State University, University Park, Pennsylvania, USA

    David Reitter

  3. Penn State University, University Park, Pennsylvania, USA

    Dongwon Lee

  4. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Nathaniel Osgood

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© 2016 Springer International Publishing Switzerland

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Zhang, L., Wu, Y., Wu, X. (2016). On Discrimination Discovery Using Causal Networks. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-39931-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39930-0

  • Online ISBN: 978-3-319-39931-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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