Over the past decade, the development of Machine Learning (ML) algorithms to replace human decisions has raised concerns about potential bias issues. At the same time, significant advances have been made in the study of fairness in classification to prevent discrimination. However, only a few of these works have investigated how those techniques can have an impact on the society and if they remain reliable over time. This work aims (i) to shed light on traditional group fairness mitigation strategies that fail in real-time environments when financial data drifts affect only some classes of sensitive attributes; (ii) to investigate a strategy that encodes the human behaviour while retraining the model over time, favoring the convergence between individual and group fairness; (iii) to put the basis of strategies based on eXplainable AI (XAI), to monitor the evolution of financial gaps between different population subgroups, like gender or race, observing whether the mitigation strategy is bringing benefits to society. Preliminary results are provided, processing about 800k personal loan granting from 2016 to 2019 for Intesa Sanpaolo bank.

Castelnovo, A., Malandri, L., Mercorio, F., Mezzanzanica, M., Cosentini, A. (2021). Towards Fairness Through Time. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp.647-663). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-93736-2_46].

Towards Fairness Through Time

Castelnovo A.;Malandri L.;Mercorio F.
;
Mezzanzanica M.;
2021

Abstract

Over the past decade, the development of Machine Learning (ML) algorithms to replace human decisions has raised concerns about potential bias issues. At the same time, significant advances have been made in the study of fairness in classification to prevent discrimination. However, only a few of these works have investigated how those techniques can have an impact on the society and if they remain reliable over time. This work aims (i) to shed light on traditional group fairness mitigation strategies that fail in real-time environments when financial data drifts affect only some classes of sensitive attributes; (ii) to investigate a strategy that encodes the human behaviour while retraining the model over time, favoring the convergence between individual and group fairness; (iii) to put the basis of strategies based on eXplainable AI (XAI), to monitor the evolution of financial gaps between different population subgroups, like gender or race, observing whether the mitigation strategy is bringing benefits to society. Preliminary results are provided, processing about 800k personal loan granting from 2016 to 2019 for Intesa Sanpaolo bank.
No
paper
Demographic Parity; Fairness; Individual fairness; Loan granting; Machine Learning; XAI;
English
21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
978-303093735-5
Castelnovo, A., Malandri, L., Mercorio, F., Mezzanzanica, M., Cosentini, A. (2021). Towards Fairness Through Time. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp.647-663). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-93736-2_46].
Castelnovo, A; Malandri, L; Mercorio, F; Mezzanzanica, M; Cosentini, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/362347
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