Abstract
Data-driven decision models based on Artificial Intelligence (AI) have been widely used in the public and private sectors. These models present challenges and are intended to be fair, effective and transparent in public interest areas. Bias, fairness and government transparency are aspects that significantly impact the functioning of a democratic society. They shape the government’s and its citizens’ relationship, influencing trust, accountability, and the equitable treatment of individuals and groups. Data-driven decision models can be biased at several process stages, contributing to injustices. Our research purpose is to understand fairness in the use of causal discovery for public procurement. By analysing Portuguese public contracts data, we aim i) to predict the place of execution of public contracts using the PC algorithm with sp_mi, smc_\(\chi \)\(^2\) and mc_\(\chi \)\(^2\) conditional independence tests; ii) to analyse and compare the fairness in those scenarios using Predictive Parity Rate, Proportional Parity, Demographic Parity and Accuracy Parity metrics. By addressing fairness concerns, we pursue to enhance responsible data-driven decision models. We conclude that, in our case, fairness metrics make an assessment more local than global due to causality pathways. We also observe that the Proportional Parity metric is the one with the lowest variance among all metrics and one with the highest precision, and this reinforces the observation that the Agency category is the one that is furthest apart in terms of the proportion of the groups.
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References
Government at a glance 2011, public procurement: Transparency in public procurement. Tech. rep., OECD (2011)
Public procurement toolbox. Tech. rep., OECD (2019)
Akter, S., Dwivedi, Y.K., Sajib, S., Biswas, K., Bandara, R.J., Michael, K.: Algorithmic bias in machine learning-based marketing models. J. Bus. Res. 144, 201–216 (2022)
Binkytė-Sadauskienė, R., Makhlouf, K., Pinzón, C., Zhioua, S., Palamidessi, C.: Causal discovery for fairness. arXiv preprint arXiv:2206.06685 (2022)
Calders, T., Verwer, S.: Three naive bayes approaches for discrimination-free classification. Data Min. Knowl. Disc. 21, 277–292 (2010)
Chouldechova, A.: Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)
Edwards, D.: Introduction to graphical modelling. Springer Science & Business Media (2012)
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. p. 259-268. KDD ’15, Association for Computing Machinery (2015)
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. p. 329-338. FAT* ’19, Association for Computing Machinery (2019)
Glymour, C., Zhang, K., Spirtes, P.: Review of causal discovery methods based on graphical models. Front. Genetics 10 (2019)
Keziou, A., Regnault, P.: Semiparametric estimation of mutual information and related criteria: optimal test of independence. IEEE Trans. Inf. Theory 63(1), 57–71 (2017)
Kozodoi, N., V. Varga, T.: fairness: Algorithmic Fairness Metrics (2021), r package version 1.2.1
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning 54(6) (2021)
Nogueira, A.R., Pugnana, A., Ruggieri, S., Pedreschi, D., Gama, J.: Methods and tools for causal discovery and causal inference. WIREs Data Min. Knowl. Discovery 12(2), e1449 (2022)
Scutari, M.: Learning bayesian networks with the bnlearn R package. J. Stat. Softw. 35(3), 1–22 (2010)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd Edition, MIT Press Books, vol. 1. The MIT Press (2001)
Teixeira, S., Rodrigues, J., Veloso, B., Gama, J.a.: An exploratory diagnosis of artificial intelligence risks for a responsible governance, pp. 25–31. Association for Computing Machinery (2022)
Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, pp. 1–7. FairWare ’18, Association for Computing Machinery (2018)
Yan, J.N., Gu, Z., Lin, H., Rzeszotarski, J.M.: Silva: Interactively assessing machine learning fairness using causality. In: Proceedings of the 2020 chi Conference on Human Factors In Computing Systems, pp. 1–13 (2020)
Zafar, M.B., Valera, I., Gomez Rodriguez, M., Gummadi, K.P.: Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment, pp. 1171–1180. WWW ’17, International World Wide Web Conferences Steering Committee (2017)
Acknowledgments
The research reported in this work was partially supported by the European Commission funded project “Humane AI: Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us" (grant #820437) and by the ERDF - European Regional Development Fund, through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme under the Portugal 2020 Partnership Agreement within project PRODUTECH4SC, with reference POCI-01-0247-FEDER-046102. The support is gratefully acknowledged.
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Teixeira, S., Nogueira, A.R., Gama, J. (2025). Fairness Analysis in Causal Models: An Application to Public Procurement. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2134. Springer, Cham. https://doi.org/10.1007/978-3-031-74627-7_14
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DOI: https://doi.org/10.1007/978-3-031-74627-7_14
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