Abstract
In this paper, we use counterfactual explanations to offer a new perspective on fairness, that, besides accuracy, accounts also for the difficulty or burden to achieve fairness. We first gather a set of fairness-related datasets and implement a classifier to extract the set of false negative test instances to generate different counterfactual explanations on them. We subsequently calculate two measures: the false negative ratio of the set of test instances, and the distance (also called burden) from these instances to their corresponding counterfactuals, aggregated by sensitive feature groups. The first measure is an accuracy-based estimation of the classifier biases against sensitive groups, whilst the second is a counterfactual-based assessment of the difficulty each of these groups has of reaching their corresponding desired ground truth label. We promote the idea that a counterfactual and an accuracy-based fairness measure may assess fairness in a more holistic manner, whilst also providing interpretability. We then propose and evaluate, on these datasets, a measure called Normalized Accuracy Weighted Burden, which is more consistent than only its accuracy or its counterfactual components alone, considering both false negative ratios and counterfactual distance per sensitive feature. We believe this measure would be more adequate to assess classifier fairness and promote the design of better performing algorithms.
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
Similar content being viewed by others
References
Boer, N., Deutch, D., Frost, N., Milo, T.: Just in time: personal temporal insights for altering model decisions. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1988–1991. IEEE (2019)
Coston, A., Mishler, A., Kennedy, E.H., Chouldechova, A.: Counterfactual risk assessments, evaluation, and fairness. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 582–593. ACM, Barcelona Spain, January 2020. https://doi.org/10.1145/3351095.3372851
Dodge, J., Liao, Q.V., Zhang, Y., Bellamy, R.K., Dugan, C.: Explaining models: an empirical study of how explanations impact fairness judgment. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 275–285 (2019)
Karimi, A.H., Barthe, G., Balle, B., Valera, I.: Model-agnostic counterfactual explanations for consequential decisions. In: International Conference on Artificial Intelligence and Statistics, pp. 895–905. PMLR (2020)
Karlsson, I., Rebane, J., Papapetrou, P., Gionis, A.: Locally and globally explainable time series tweaking. Knowl. Inf. Syst. 62(5), 1671–1700 (2020)
Kearns, M., Neel, S., Roth, A., Wu, Z.S.: An empirical study of rich subgroup fairness for machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 100–109 (2019)
Kuratomi, A., Lindgren, T., Papapetrou, P.: Prediction of global navigation satellite system positioning errors with guarantees. In: Dong, Y., Mladenić, D., Saunders, C. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12460, pp. 562–578. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67667-4_34
Kusner, M.J., Loftus, J.R., Russell, C., Silva, R.: Counterfactual fairness. arXiv:1703.06856 [cs, stat], March 2018. http://arxiv.org/1703.06856
Kyrimi, E., Neves, M.R., McLachlan, S., Neil, M., Marsh, W., Fenton, N.: Medical idioms for clinical Bayesian network development. J. Biomed. Inform. 108, 103495 (2020)
Laugel, T., Lesot, M.J., Marsala, C., Renard, X., Detyniecki, M.: Inverse classification for comparison-based interpretability in machine learning. arXiv preprint arXiv:1712.08443 (2017)
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., Detyniecki, M.: Unjustified classification regions and counterfactual explanations in machine learning. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 37–54. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46147-8_3
Lindgren, T., Papapetrou, P., Samsten, I., Asker, L.: Example-based feature tweaking using random forests. In: 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), pp. 53–60. IEEE (2019)
Loi, M., Ferrario, A., Viganò, E.: Transparency as design publicity: explaining and justifying inscrutable algorithms. Ethics Inf. Technol. 23(3), 253–263 (2021). https://doi.org/10.1007/s10676-020-09564-w
Molnar, C.: Interpretable machine learning: a guide for making black-box models explainable (2021). https://christophm.github.io/interpretable-ml-book/limo.html
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617 (2020)
Nobrega, C., Marinho, L.: Towards explaining recommendations through local surrogate models. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. SAC 2019, pp. 1671–1678. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3297280.3297443
Pawelczyk, M., Broelemann, K., Kasneci, G.: Learning model-agnostic counterfactual explanations for tabular data. In: Proceedings of The Web Conference 2020, pp. 3126–3132 (2020)
Pitoura, E., Stefanidis, K., Koutrika, G.: Fairness in rankings and recommendations: an overview. VLDB J. (Oct2021)
Pitoura, E., et al.: On Measuring bias in online information. ACM SIGMOD Rec. 46(4), 16–21 (2018)
Quy, T.L., Roy, A., Iosifidis, V., Zhang, W., Ntoutsi, E.: A survey on datasets for fairness-aware machine learning. arXiv:2110.00530 [cs] (Jan 2022). https://arxiv.org/abs/2110.00530
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)
Sharma, S., Henderson, J., Ghosh, J.: CERTIFAI: counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 166–172, Februay 2020. https://doi.org/10.1145/3375627.3375812, arXiv:1905.07857
Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable predictions of tree-based ensembles via actionable feature tweaking. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 465–474 (2017)
Tsintzou, V., Pitoura, E., Tsaparas, P.: Bias disparity in recommendation systems. arXiv:1811.01461 [cs], November 2018, https://arxiv.org/abs/1811.01461
Ustun, B., Spangher, A., Liu, Y.: Actionable recourse in linear classification. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 10–19 (2019)
Verma, S., Dickerson, J., Hines, K.: Counterfactual explanations for machine learning: a review. arXiv:2010.10596 [cs, stat], October 2020. https://arxiv.org/abs/2010.10596
Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F., Wilson, J.: The what-if tool: interactive probing of machine learning models. IEEE Trans. Vis. Comput. Graph. 26(1), 56–65 (2019)
Zafar, M.B., Valera, I., Rodriguez, M.G., Gummadi, K.P.: Fairness constraints: mechanisms for fair classification. arXiv:1507.05259 [cs, stat], March 2017. https://arxiv.org/abs/1507.05259
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kuratomi, A., Pitoura, E., Papapetrou, P., Lindgren, T., Tsaparas, P. (2023). Measuring the Burden of (Un)fairness Using Counterfactuals. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-23618-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23617-4
Online ISBN: 978-3-031-23618-1
eBook Packages: Computer ScienceComputer Science (R0)