Skip to main content
SpringerLink
Log in

Towards Fairness in AI: Addressing Bias in Data Using GANs

  • Conference paper
  • First Online:
HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13095))

Included in the following conference series:

  • 1968 Accesses

Abstract

Can we trust machine learning models to make fair decisions? This question becomes more relevant as these algorithms become more pervasive in many aspects of our lives and our society. While the main objective of artificial intelligence (AI) algorithms is traditionally to increase accuracy, the AI community is gradually focusing more on evaluating and developing algorithms to ensure fairness. This work explores the usefulness of adversarial learning, explicitly generative adversarial networks (GAN), in addressing the problem of fairness. We show that the proposed model is able to produce synthetic tabular data to augment the original dataset in order to improve demographic parity, while maintaining data utility. In doing so, our work increases algorithmic fairness while maintaining accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    http://archive.ics.uci.edu/ml/datasets/adult.

References

  1. Agarwal, A., Dudik, M., Wu, Z.S.: Fair regression: quantitative definitions and reduction-based algorithms. In: International Conference on Machine Learning, pp. 120–129. PMLR (2019)

    Google Scholar 

  2. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)

    Google Scholar 

  3. Barocas, S., Hardt, M., Narayanan, A.: Fairness in machine learning. In: NIPS Tutorial, vol. 1, p. 2 (2017)

    Google Scholar 

  4. Bolukbasi, T., Chang, K.W., Zou, J., Saligrama, V., Kalai, A.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. arXiv preprint arXiv:1607.06520 (2016)

  5. Brunet, M.E., Alkalay-Houlihan, C., Anderson, A., Zemel, R.: Understanding the origins of bias in word embeddings. In: International Conference on Machine Learning, pp. 803–811. PMLR (2019)

    Google Scholar 

  6. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pp. 77–91. PMLR (2018)

    Google Scholar 

  7. Calders, T., Verwer, S.: Three Naive Bayes approaches for discrimination-free classification. Data Mini. Knowl. Discov. 21(2), 277–292 (2010). https://doi.org/10.1007/s10618-010-0190-x

    Article  MathSciNet  Google Scholar 

  8. Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)

    Article  Google Scholar 

  9. Chouldechova, A., Roth, A.: The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810 (2018)

  10. d’Alessandro, B., O’Neil, C., LaGatta, T.: Conscientious classification: a data scientist’s guide to discrimination-aware classification. Big Data 5(2), 120–134 (2017)

    Article  Google Scholar 

  11. Dua, D., Graff, C.: UCI machine learning repository (2017)

    Google Scholar 

  12. 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 (2012)

    Google Scholar 

  13. Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259–268 (2015)

    Google Scholar 

  14. Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 329–338 (2019)

    Google Scholar 

  15. Goodfellow, I.J., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)

  16. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017)

  17. Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. arXiv preprint arXiv:1610.02413 (2016)

  18. Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)

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

    Article  Google Scholar 

  20. Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Fairness-aware classifier with prejudice remover regularizer. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 35–50. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33486-3_3

    Chapter  Google Scholar 

  21. Lambrecht, A., Tucker, C.: Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of stem career ads. Manage. Sci. 65(7), 2966–2981 (2019)

    Article  Google Scholar 

  22. Lepri, B., Oliver, N., Letouzé, E., Pentland, A., Vinck, P.: Fair, transparent, and accountable algorithmic decision-making processes. Philos. Technol. 31(4), 611–627 (2018)

    Article  Google Scholar 

  23. Menéndez, M.L., Pardo, J.A., Pardo, L., Pardo, M.C.: The Jensen-Shannon divergence. J. Franklin Inst. 334(2), 307–318 (1997)

    Article  MathSciNet  Google Scholar 

  24. O’neil, C.: Weapons of math destruction: how big data increases inequality and threatens democracy. Crown (2016)

    Google Scholar 

  25. Oneto, L., Chiappa, S.: Fairness in machine learning. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds.) Recent Trends in Learning From Data. SCI, vol. 896, pp. 155–196. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43883-8_7

    Chapter  Google Scholar 

  26. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  27. Pedreshi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 560–568 (2008)

    Google Scholar 

  28. Pessach, D., Shmueli, E.: Algorithmic fairness. arXiv preprint arXiv:2001.09784 (2020)

  29. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    Article  Google Scholar 

  30. Schermer, B.W.: The limits of privacy in automated profiling and data mining. Comput. Law Secur. Rev. 27(1), 45–52 (2011)

    Article  Google Scholar 

  31. Wadsworth, C., Vera, F., Piech, C.: Achieving fairness through adversarial learning: an application to recidivism prediction. arXiv preprint arXiv:1807.00199 (2018)

  32. Xu, D., Yuan, S., Zhang, L., Wu, X.: FairGAN: fairness-aware generative adversarial networks. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 570–575. IEEE (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA

    Amirarsalan Rajabi

  2. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816, USA

    Ozlem O. Garibay

Authors
  1. Amirarsalan Rajabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ozlem O. Garibay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ozlem O. Garibay .

Editor information

Editors and Affiliations

  1. University of Creteand Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  3. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA

    Jessie Y. C. Chen

  4. U.S. Army Combat Capabilities Development Command Soldier Center, Orlando, FL, USA

    Gino Fragomeni

  5. Smart Future Initiative, Frankfurt am Main, Hessen, Germany

    Norbert Streitz

  6. Faculty of Arts and Science, Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

  7. Siemens (United States), Princeton, NJ, USA

    Helmut Degen

  8. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rajabi, A., Garibay, O.O. (2021). Towards Fairness in AI: Addressing Bias in Data Using GANs. In: Stephanidis, C., et al. HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence. HCII 2021. Lecture Notes in Computer Science(), vol 13095. Springer, Cham. https://doi.org/10.1007/978-3-030-90963-5_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-90963-5_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90962-8

  • Online ISBN: 978-3-030-90963-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation