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Fairness Analysis in Causal Models: An Application to Public Procurement

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)

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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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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Correspondence to Sónia Teixeira .

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Fig. 5.
figure 5

Data distribution for variables Republic Assembly, Local Government, Madeira Regional Legislative Assembly and Açores Regional Legislative Assembly

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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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