Skip to main content

\(\mathsf {FABBOO}\) - Online Fairness-Aware Learning Under Class Imbalance

  • Conference paper
  • First Online:
Discovery Science (DS 2020)

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

Included in the following conference series:


Data-driven algorithms are employed in many applications, in which data become available in a sequential order, forcing the update of the model with new instances. In such dynamic environments, in which the underlying data distributions might evolve with time, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class distribution skewness. As a result, such methods mitigate discrimination by “rejecting” minority instances at large due to their inability to effectively learn all classes. In this work, we propose \(\mathsf {FABBOO}\), an online fairness-aware approach that maintains a valid and fair classifier over a stream. \(\mathsf {FABBOO}\) is an online boosting approach that changes the training distribution in an online fashion based on both stream imbalance and discriminatory behavior of the model evaluated over the historical stream. Our experiments show that such long-term consideration of class-imbalance and fairness are beneficial for maintaining models that exhibit good predictive- and fairness-related performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

    SA definition could also be extended to cover feature combinations such as race and gender.

  2. 2.


  1. Ali, M., et al.: Discrimination through optimization: how facebook’s ad delivery can lead to skewed outcomes. arXiv preprint arXiv:1904.02095 (2019)

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

    Google Scholar 

  3. Bifet, A., Gavaldà, R.: Adaptive learning from evolving data streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 249–260. Springer, Heidelberg (2009).

    Chapter  Google Scholar 

  4. Calders, T., Žliobaitė, I.: Why unbiased computational processes can lead to discriminative decision procedures. In: Custers, B., Calders, T., Schermer, B., Zarsky, T. (eds.) Studies in Applied Philosophy, Epistemology and Rational Ethics. Discrimination and Privacy in the Information Society, vol. 3, pp. 43–57. Springer, Heidelberg (2013).

  5. Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K.N., Varshney, K.R.: Optimized pre-processing for discrimination prevention. In: Advances in Neural Information Processing Systems, pp. 3992–4001 (2017)

    Google Scholar 

  6. Chapman, D., Ryan, P., Farmer, J.P.: Introducing Office Sci. Technol. Policy (2013).

  7. Chen, S.T., Lin, H.T., Lu, C.J.: An online boosting algorithm with theoretical justifications. arXiv preprint arXiv:1206.6422 (2012)

  8. Cortez, V.: Preventing discriminatory outcomes in credit models (2019).

  9. Council, N.R., et al.: Measuring Racial Discrimination. National Academies Press, Washington, DC (2004)

    Google Scholar 

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

    Article  Google Scholar 

  11. Ditzler, G., Polikar, R.: Incremental learning of concept drift from streaming imbalanced data. IEEE Trans. Knowl. Data Eng. 25(10), 2283–2301 (2012)

    Article  Google Scholar 

  12. Fish, B., Kun, J., Lelkes, Á.D.: A confidence-based approach for balancing fairness and accuracy. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 144–152. SIAM (2016)

    Google Scholar 

  13. Forman, G.: Tackling concept drift by temporal inductive transfer. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 252–259. ACM (2006)

    Google Scholar 

  14. Gama, J.: Knowledge Discovery from Data Streams. Chapman and Hall/CRC, New York (2010)

    Book  Google Scholar 

  15. Grace, K., Salvatier, J., Dafoe, A., Zhang, B., Evans, O.: When will ai exceed human performance? evidence from ai experts. J. Artif. Intell. Res. 62, 729–754 (2018)

    Article  MathSciNet  Google Scholar 

  16. Hardt, M., et al.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, pp. 3315–3323 (2016)

    Google Scholar 

  17. Hu, H., et al.: Fairnn-conjoint learning of fair representations for fair decisions. arXiv preprint arXiv:2004.02173 (2020)

  18. Ingold, D., Soper, S.: Amazon Doesn’t Consider the Race of Its Customers. Should It, Bloomberg (2016)

    Google Scholar 

  19. 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 

  20. Iosifidis, V., Ntoutsi, E.: Dealing with bias via data augmentation in supervised learning scenarios.In: Bates, J., Clough, P.D., Jäschke, R., Otterbacher, J.: International Workshop on Bias in Information, Algorithms, and Systems (BIAS). Proceedings of the International Workshop on Bias in Information, Algorithms, and Systems (BIAS). CEUR Workshop Proceedings, pp. 24–29 (2018).

  21. Iosifidis, V., Ntoutsi, E.: Adafair: cumulative fairness adaptive boosting. In: CIKM (2019)

    Google Scholar 

  22. Iosifidis, V., Tran, T.N.H., Ntoutsi, E.: Fairness-Enhancing Interventions in Stream Classification. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DEXA 2019. LNCS, vol. 11706, pp. 261–276. Springer, Cham (2019).

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  25. Krasanakis, E., Xioufis, E.S., Papadopoulos, S., Kompatsiaris, Y.: Adaptive sensitive reweighting to mitigate bias in fairness-aware classification. In: WWW, pp. 853–862. ACM (2018)

    Google Scholar 

  26. Vafa, K., Haigh, C., Leung, A., Yonack, N.: Price discrimination in the princeton review’s online sat tutoring service. JOTS Technol, Sci (2015)

    Google Scholar 

  27. Verma, S., Rubin, J.: Fairness definitions explained. In: 2018 IEEE/ACM International Workshop on Software Fairness (FairWare), pp. 1–7. IEEE (2018)

    Google Scholar 

  28. Wang, S., Minku, L.L., Yao, X.: A learning framework for online class imbalance learning. In: 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), pp. 36–45. IEEE (2013)

    Google Scholar 

  29. Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explor. Newsl. 6(1), 7–19 (2004)

    Article  Google Scholar 

  30. Wenbin, Z., Ntoutsi, E.: Faht: an adaptive fairness-aware decision tree classifier. arXiv preprint arXiv:1907.07237 (2019)

  31. 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, pp. 1171–1180. WWW (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Vasileios Iosifidis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Iosifidis, V., Ntoutsi, E. (2020). \(\mathsf {FABBOO}\) - Online Fairness-Aware Learning Under Class Imbalance. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61526-0

  • Online ISBN: 978-3-030-61527-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics