Abstract
Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables, a novel approach able to enhance fairness in both binary and multi-class classification problems. The proposed method is compared, under several conditions, with the well-established baseline. We evaluate our method on a heterogeneous data set and prove how it overcomes the established algorithms in the multi-classification setting, while maintaining good performances in binary classification. Finally, we present some limitations and future improvements.
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
Notes
- 1.
The variables can be binary, discrete or categorical ones.
References
AI Fairness 360 - Resources. https://aif360.mybluemix.net/resources#guidance
Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., Wallach, H.: A reductions approach to fair classification. arXiv:1803.02453 [cs], July 2018. arXiv: 1803.02453
Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine Bias. ProPublica, pp. 139–159, 23 May 2016
Austin, K.A., Christopher, C.M., Dickerson, D.: Will I pass the bar exam: Will I pass the bar exam: predicting student success using LSAT scores and law school performance. HofstrA l. Rev. 45, 753 (2016)
Bird, S., et al.: Fairlearn: a toolkit for assessing and improving fairness in AI. Tech. Rep. MSR-TR-2020-32, Microsoft, May 2020. https://www.microsoft.com/en-us/research/publication/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai/
Calders, T., Karim, A., Kamiran, F., Ali, W., Zhang, X.: Controlling attribute effect in linear regression. In: 2013 IEEE 13th International Conference on Data Mining, pp. 71–00, December 2013. https://doi.org/10.1109/ICDM.2013.114, ISSN: 2374-8486
Caton, S., Haas, C.: Fairness in machine learning: a survey. arXiv:2010.04053 [cs, stat] (October 2020), arXiv: 2010.04053
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Modeling wine preferences by data mining from physicochemical properties. Decis. Supp. Syst. 47(4), 547–553 (2009)
DaliaResearch: The Trump Effect in Europe (2017). https://www.kaggle.com/daliaresearch/trump-effect
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learning 29(2), 103–130 (1997)
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. ITCS ’12, Association for Computing Machinery, New York, NY, USA, January 2012. https://doi.org/10.1145/2090236.2090255
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. ACM, Sydney NSW Australia, Aug 2015. https://doi.org/10.1145/2783258.2783311
Friedler, S.A., Scheidegger, C., Venkatasubramanian, S.: On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236 (2016)
Hajian, S., Bonchi, F., Castillo, C.: Algorithmic bias: from discrimination discovery to fairness-aware data mining. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 2125–2126. ACM (2016)
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Adv. Neural Inf. Process. Syst. 29, 3315–3323 (2016)
Jiang, C., Liu, Y., Ding, Y., Liang, K., Duan, R.: Capturing helpful reviews from social media for product quality improvement: a multi-class classification approach. Int. J. Prod. Res. 55(12), 3528–3541 (2017)
Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1–33 (2012). https://doi.org/10.1007/s10115-011-0463-8
Kohavi, R., et al.: Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid. In: KDD’96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207 (1996)
Krishnaswamy, A., Jiang, Z., Wang, K., Cheng, Y., Munagala, K.: Fair for all: best-effort fairness guarantees for classification. In: International Conference on Artificial Intelligence and Statistics, pp. 3259–3267. PMLR (2021)
Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017)
Le, Y., He, C., Chen, M., Wu, Y., He, X., Zhou, B.: Learning to predict charges for legal judgment via self-attentive capsule network. In: ECAI 2020, pp. 1802–1809. IOS Press, Red Hook (2020)
Lim, T.S., Loh, W.Y., Shih, Y.S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40(3), 203–228 (2000)
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Compu. Surv. 54(6), 1–35 (2021). https://doi.org/10.1145/3457607
Radovanović, S., Petrović, A., Delibašić, B., Suknović, M.: A fair classifier chain for multi-label bank marketing strategy classification. Int. Trans. Oper. Res. (2021). https://doi.org/10.1111/itor.13059, https://onlinelibrary.wiley.com/doi/pdf/10.1111/itor.13059
Ratanamahatana, C.A., Gunopulos, D.: Scaling up the naive Bayesian classifier: Using decision trees for feature selection. Appl. Artiff. Intell. 17(5), 475–487 (2002)
Redmond, M., Baveja, A.: A data-driven software tool for enabling cooperative information sharing among police departments. Eur. J. Oper. Res. 141(3), 660–678 (2002)
Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation, pp. 1–7. Springer, New York (2016). https://doi.org/10.1007/978-1-4899-7993-3_565-2
Rosenfield, G., Fitzpatrick-Lins, K.: A coefficient of agreement as a measure of thematic classification accuracy. Photogram. Eng. Rem. Sen. 52(2), 223–227 (1986). http://pubs.er.usgs.gov/publication/70014667
Sánchez-Morillo, D., López-Gordo, M., León, A.: Novel multiclass classification for home-based diagnosis of sleep apnea hypopnea syndrome. Exp. Syst. Appli. 41(4), 1654–1662 (2014)
Street, W.N., Wolberg, W.H., Mangasarian, O.L.: Nuclear feature extraction for breast tumor diagnosis. In: Biomedical Image Processing and Biomedical Visualization. vol. 1905, pp. 861–870. International Society for Optics and Photonics (1993)
Wolpert, D.H.: What does dinner cost? http://www.no-free-lunch.org/coev.pdf
Acknowledgments
This work is partially supported by Territori Aperti a project funded by Fondo Territori Lavoro e Conoscenza CGIL CISL UIL, by SoBigData-PlusPlus H2020-INFRAIA-2019-1 EU project, contract number 871042 and by “FAIR-EDU: Promote FAIRness in EDUcation institutions” a project founded by the University of L’Aquila.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
d’Aloisio, G., Stilo, G., Di Marco, A., D’Angelo, A. (2022). Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol 1610. Springer, Cham. https://doi.org/10.1007/978-3-031-09316-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-09316-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09315-9
Online ISBN: 978-3-031-09316-6
eBook Packages: Computer ScienceComputer Science (R0)