Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 35–50Cite as

  1. Home
  2. Machine Learning and Knowledge Discovery in Databases
  3. Conference paper
Fairness-Aware Classifier with Prejudice Remover Regularizer

Fairness-Aware Classifier with Prejudice Remover Regularizer

  • Toshihiro Kamishima21,
  • Shotaro Akaho21,
  • Hideki Asoh21 &
  • …
  • Jun Sakuma22,23 
  • Conference paper
  • 9987 Accesses

  • 229 Citations

  • 18 Altmetric

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7524)

Abstract

With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals’ lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.

Keywords

  • fairness
  • discrimination
  • logistic regression
  • classification
  • social responsibility
  • information theory

Download conference paper PDF

References

  1. Aggarwal, C.C., Yu, P.S. (eds.): Privacy-Preserving Data Mining: Models and Algorithms. Springer (2008)

    Google Scholar 

  2. Boyd, D.: Privacy and publicity in the context of big data. In: Keynote Talk of The 19th Int’l Conf. on World Wide Web (2010)

    Google Scholar 

  3. Calders, T., Verwer, S.: Three naive bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery 21, 277–292 (2010)

    CrossRef  MathSciNet  Google Scholar 

  4. Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proc. of the 24th Int’l Conf. on Machine Learning, pp. 193–200 (2007)

    Google Scholar 

  5. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. arxiv.org:1104.3913 (2011)

    Google Scholar 

  6. Elkan, C.: The foundations of cost-sensitive learning. In: Proc. of the 17th Int’l Joint Conf. on Artificial Intelligence, pp. 973–978 (2001)

    Google Scholar 

  7. Frank, A., Asuncion, A.: UCI machine learning repository. School of Information and Computer Sciences, University of California, Irvine (2010), http://archive.ics.uci.edu/ml

    Google Scholar 

  8. Gondek, D., Hofmann, T.: Non-redundant data clustering. In: Proc. of the 4th IEEE Int’l Conf. on Data Mining, pp. 75–82 (2004)

    Google Scholar 

  9. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience (2001)

    Google Scholar 

  10. NIPS workshop — inductive transfer: 10 years later (2005), http://iitrl.acadiau.ca/itws05/

  11. Kamiran, F., Calders, T., Pechenizkiy, M.: Discrimination aware decision tree learning. In: Proc. of the 10th IEEE Int’l Conf. on Data Mining, pp. 869–874 (2010)

    Google Scholar 

  12. Kamishima, T., Akaho, S., Sakuma, J.: Fairness-aware learning through regularization approach. In: Proc. of The 3rd IEEE Int’l Workshop on Privacy Aspects of Data Mining, pp. 643–650 (2011)

    Google Scholar 

  13. Luong, B.T., Ruggieri, S., Turini, F.: k-NN as an implementation of situation testing for discrimination discovery and prevention. In: Proc. of the 17th Int’l Conf. on Knowledge Discovery and Data Mining, pp. 502–510 (2011)

    Google Scholar 

  14. Nissim, K.: Private data analysis via output perturbation. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining: Models and Algorithms, ch. 4. Springer (2008)

    Google Scholar 

  15. Pariser, E.: The Filter Bubble: What The Internet Is Hiding From You. Viking (2011)

    Google Scholar 

  16. Pearl, J.: Causality: Models, Reasoning and Inference, 2nd edn. Cambridge University Press (2009)

    Google Scholar 

  17. Pedreschi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proc. of the 14th Int’l Conf. on Knowledge Discovery and Data Mining (2008)

    Google Scholar 

  18. Pedreschi, D., Ruggieri, S., Turini, F.: Measuring discrimination in socially-sensitive decision records. In: Proc. of the SIAM Int’l Conf. on Data Mining, pp. 581–592 (2009)

    Google Scholar 

  19. Perlich, C., Kaufman, S., Rosset, S.: Leakage in data mining: Formulation, detection, and avoidance. In: Proc. of the 17th Int’l Conf. on Knowledge Discovery and Data Mining, pp. 556–563 (2011)

    Google Scholar 

  20. Ruggieri, S., Pedreschi, D., Turini, F.: DCUBE: Discrimination discovery in databases. In: Proc of The ACM SIGMOD Int’l Conf. on Management of Data, pp. 1127–1130 (2010)

    Google Scholar 

  21. Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583–617 (2002)

    MathSciNet  Google Scholar 

  22. Venkatasubramanian, S.: Measures of anonimity. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining: Models and Algorithms, ch. 4. Springer (2008)

    Google Scholar 

  23. Žliobaitė, I., Kamiran, F., Calders, T.: Handling conditional discrimination. In: Proc. of the 11th IEEE Int’l Conf. on Data Mining (2011)

    Google Scholar 

  24. Zadrozny, B.: Learning and evaluating classifiers under sample selection bias. In: Proc. of the 21st Int’l Conf. on Machine Learning, pp. 903–910 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 2, Umezono 1-1-1, Tsukuba, Ibaraki, 305-8568, Japan

    Toshihiro Kamishima, Shotaro Akaho & Hideki Asoh

  2. University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8577, Japan

    Jun Sakuma

  3. Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi, Saitama, 332-0012, Japan

    Jun Sakuma

Authors
  1. Toshihiro Kamishima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shotaro Akaho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hideki Asoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Sakuma
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach

  2. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road,, BS8 1UB, Bristol, UK

    Tijl De Bie & Nello Cristianini & 

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kamishima, T., Akaho, S., Asoh, H., Sakuma, J. (2012). Fairness-Aware Classifier with Prejudice Remover Regularizer. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_3

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33486-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33485-6

  • Online ISBN: 978-3-642-33486-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature