Abstract
As opposed to group fairness algorithms which enforce equality of distributions, individual fairness aims at treating similar people similarly. In this paper, we focus on individual fairness regarding sensitive attributes that should be removed from people comparisons. In that aim, we present a new method that leverages the Variational Autoencoder (VAE) algorithm and the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation coefficient for enforcing individual fairness in predictions. We also propose new metrics to assess individual fairness. We demonstrate the effectiveness of our approach in enforcing individual fairness on several machine learning tasks prone to algorithmic bias.
V. Grari and O. El Hajouji—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adel, T., Valera, I., Ghahramani, Z., Weller, A.: One-network adversarial fairness. In: AAAI 2019, vol. 33, pp. 2412–2420 (2019)
Calders, T., Kamiran, F., Pechenizkiy, M.: Building classifiers with independency constraints. In: ICDM Workshops, pp. 13–18. IEEE (2009)
Chen, X., et al.: Variational lossy autoencoder. arXiv preprint arXiv:1611.02731 (2016)
Chiappa, S.: Path-specific counterfactual fairness. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801–7808 (2019)
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: ITCS 2012, pp. 214–226 (2012)
Gillen, S., Jung, C., Kearns, M., Roth, A.: Online learning with an unknown fairness metric. In: Advances in Neural Information Processing Systems, pp. 2600–2609 (2018)
Grari, V., Hajouji, O.E., Lamprier, S., Detyniecki, M.: Learning unbiased representations via rényi minimization (2020)
Grari, V., Lamprier, S., Detyniecki, M.: Fairness-aware neural rényi minimization for continuous features. In: Bessiere, C. (ed.) IJCAI 2020, pp. 2262–2268. ijcai.org (2020)
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012)
Ilvento, C.: Metric learning for individual fairness. arXiv preprint arXiv:1906.00250 (2019)
Jung, C., Kearns, M., Neel, S., Roth, A., Stapleton, L., Wu, Z.S.: Eliciting and enforcing subjective individual fairness. arXiv preprint arXiv:1905.10660 (2019)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.: The variational fair autoencoder. arXiv preprint arXiv:1511.00830 (2015)
Madras, D., Creager, E., Pitassi, T., Zemel, R.: Fairness through causal awareness: Learning causal latent-variable models for biased data. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 349–358 (2019)
Mukherjee, D., Yurochkin, M., Banerjee, M., Sun, Y.: Two simple ways to learn individual fairness metrics from data. arXiv preprint arXiv:2006.11439 (2020)
Pfohl, S., Duan, T., Ding, D.Y., Shah, N.H.: Counterfactual reasoning for fair clinical risk prediction. arXiv preprint arXiv:1907.06260 (2019)
Rényi, A.: On measures of dependence. Acta Mathematica Hungarica 10(3–4), 441–451 (1959)
Yurochkin, M., Bower, A., Sun, Y.: Training individually fair ml models with sensitive subspace robustness. In: International Conference on Learning Representations (2019)
Yurochkin, M., Sun, Y.: Sensei: Sensitive set invariance for enforcing individual fairness. arXiv preprint arXiv:2006.14168 (2020)
Zafar, M.B., Valera, I., Rogriguez, M.G., Gummadi, K.P.: Fairness constraints: mechanisms for fair classification. In: AISTATS 2017, pp. 962–970. Fort Lauderdale, FL, USA, April 2017
Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: AAAI 2018, pp. 335–340 (2018)
Zhao, S., Song, J., Ermon, S.: Infovae: Information maximizing variational autoencoders. arXiv preprint arXiv:1706.02262 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Grari, V., El Hajouji, O., Lamprier, S., Detyniecki, M. (2021). Enforcing Individual Fairness via Rényi Variational Inference. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-92307-5_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92306-8
Online ISBN: 978-3-030-92307-5
eBook Packages: Computer ScienceComputer Science (R0)