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FairNN - Conjoint Learning of Fair Representations for Fair Decisions

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12323)


In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing the reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularizer. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making.


  • Fairness
  • Bias
  • Neural networks
  • Auto-encoders

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  1. 1.


  1. Bache, K., Lichman, M.: UCI machine learning repository (2013)

    Google Scholar 

  2. Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K.N., Varshney, K.R.: Optimized pre-processing for discrimination prevention. In: NeurIPS, pp. 3992–4001 (2017)

    Google Scholar 

  3. Datta, A., Tschantz, M.C., Datta, A.: Automated experiments on ad privacy settings. Priv. Enhanc. Technol. 2015(1), 92–112 (2015)

    CrossRef  Google Scholar 

  4. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.S.: Fairness through awareness. In: Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, 8–10 January 2012, pp. 214–226 (2012)

    Google Scholar 

  5. Edelman, B.G., Luca, M.: Digital discrimination: The case of airbnb. com (2014)

    Google Scholar 

  6. Edwards, H., Storkey, A.J.: Censoring representations with an adversary. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016, Conference Track Proceedings (2016)

    Google Scholar 

  7. Fish, B., Kun, J., Lelkes, Á.D.: A confidence-based approach for balancing fairness and accuracy. In: Venkatasubramanian, S.C., Jr, W.M. (eds.) Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, Florida, USA, 5–7 May 2016, pp. 144–152. SIAM (2016)

    Google Scholar 

  8. Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain, 5–10 December 2016, pp. 3315–3323 (2016)

    Google Scholar 

  9. Ingold, D., Soper, S.: Amazon doesn’t consider the race of its customers. should it. Bloomberg, April 2016

    Google Scholar 

  10. Iosifidis, V., Fetahu, B., Ntoutsi, E.: FAE: a fairness-aware ensemble framework. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1375–1380. IEEE (2019)

    Google Scholar 

  11. Iosifidis, V., Ntoutsi, E.: Dealing with bias via data augmentation in supervised learning scenarios. Jo Bates Paul D. Clough Robert Jäschke, p. 24 (2018)

    Google Scholar 

  12. Iosifidis, V., Ntoutsi, E.: AdaFair: cumulative fairness adaptive boosting. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, 3–7 November 2019, pp. 781–790 (2019)

    Google Scholar 

  13. Iosifidis, V., Tran, T.N.H., Ntoutsi, E.: Fairness-enhancing interventions in stream classification. In: Database and Expert Systems Applications - 30th International Conference, DEXA 2019, Linz, Austria, August 26–29 2019, Proceedings, Part I, pp. 261–276 (2019)

    Google Scholar 

  14. Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1–33 (2011)

    CrossRef  Google Scholar 

  15. Kamiran, F., Calders, T., Pechenizkiy, M.: Discrimination aware decision tree learning. In: ICDM, pp. 869–874. IEEE Computer Society (2010)

    Google Scholar 

  16. Kamiran, F., Mansha, S., Karim, A., Zhang, X.: Exploiting reject option in classification for social discrimination control. Inf. Sci. 425, 18–33 (2018)

    CrossRef  MathSciNet  Google Scholar 

  17. Krasanakis, E., Xioufis, E.S., Papadopoulos, S., Kompatsiaris, Y.: Adaptive sensitive reweighting to mitigate bias in fairness-aware classification. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, 23–27 April 2018, pp. 853–862 (2018)

    Google Scholar 

  18. Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.S.: The variational fair autoencoder. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016, Conference Track Proceedings (2016)

    Google Scholar 

  19. Madras, D., Creager, E., Pitassi, T., Zemel, R.: Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309 (2018)

  20. Manisha, P., Gujar, S.: A neural network framework for fair classifier. arXiv preprint arXiv:1811.00247 (2018)

  21. Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22–31 (2014)

    CrossRef  Google Scholar 

  22. Navarin, N., Oneto, L., Donini, M.: Learning deep fair graph neural networks (2020)

    Google Scholar 

  23. Ntoutsi, E., et al.: Bias in data-driven artificial intelligence systems - an introductory survey. WIREs Data Mining and Knowledge Discovery (2020)

    Google Scholar 

  24. Oneto, L., Donini, M., Maurer, A., Pontil, M.: Learning fair and transferable representations. arXiv preprint arXiv:1906.10673 (2019)

  25. U.S.E.O. of the President, Podesta, J.: Big data: Seizing opportunities, preserving values. White House, Executive Office of the President (2014)

    Google Scholar 

  26. Rudolph, M., Wandt, B., Rosenhahn, B.: Structuring autoencoders. In: Third International Workshop on “Robust Subspace Learning and Applications in Computer Vision” (ICCV), August 2019

    Google Scholar 

  27. Ruoss, A., Balunovic, M., Fischer, M., Vechev, M.T.: Learning certified individually fair representations. CoRR abs/2002.10312 (2020)

    Google Scholar 

  28. Samadi, S., Tantipongpipat, U.T., Morgenstern, J.H., Singh, M., Vempala, S.S.: The price of fair PCA: one extra dimension. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, Montréal, Canada, December 3–8 2018, pp. 10999–11010 (2018)

    Google Scholar 

  29. Zafar, M.B., Valera, I., Gomez-Rodriguez, M., Gummadi, K.P.: Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, 3–7 April 2017, pp. 1171–1180 (2017)

    Google Scholar 

  30. Zhang, W., Ntoutsi, E.: FAHT: an adaptive fairness-aware decision tree classifier. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10–16 August 2019, pp. 1480–1486 (2019)

    Google Scholar 

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The work is supported by BIAS (Bias and Discrimination in Big Data and Algorithmic Processing. Philosophical Assessments, Legal Dimensions, and Technical Solutions) a project funded by the Volkswagen Foundation within the initiative AI and the Society of the Future for which the last authors are Principal Investigators.

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Correspondence to Wentong Liao .

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Hu, T. et al. (2020). FairNN - Conjoint Learning of Fair Representations for Fair Decisions. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61526-0

  • Online ISBN: 978-3-030-61527-7

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