Algorithmic bias mitigation has been one of the most difficult conundrums for the data science community and Machine Learning (ML) experts. Over several years, there have appeared enormous efforts in the field of fairness in ML. Despite the progress toward identifying biases and designing fair algorithms, translating them into the industry remains a major challenge. In this paper, we present the initial results of an industrial open innovation project in the banking sector: we propose a general roadmap for fairness in ML and the implementation of a toolkit called BeFair that helps to identify and mitigate bias. Results show that training a model without explicit constraints may lead to bias exacerbation in the predictions.

Castelnovo, A., Crupi, R., Gamba, G., Greco, G., Naseer, A., Regoli, D., et al. (2020). BeFair: Addressing Fairness in the Banking Sector. In Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp.3652-3661). New York : Institute of Electrical and Electronics Engineers Inc. [10.1109/BigData50022.2020.9377894].

BeFair: Addressing Fairness in the Banking Sector

Castelnovo A.;Greco G.;
2020

Abstract

Algorithmic bias mitigation has been one of the most difficult conundrums for the data science community and Machine Learning (ML) experts. Over several years, there have appeared enormous efforts in the field of fairness in ML. Despite the progress toward identifying biases and designing fair algorithms, translating them into the industry remains a major challenge. In this paper, we present the initial results of an industrial open innovation project in the banking sector: we propose a general roadmap for fairness in ML and the implementation of a toolkit called BeFair that helps to identify and mitigate bias. Results show that training a model without explicit constraints may lead to bias exacerbation in the predictions.
slide + paper
bias mitigation; discrimination; fairness; machine learning
English
8th IEEE International Conference on Big Data, Big Data 2020
2020
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
978-1-7281-6251-5
2020
3652
3661
9377894
open
Castelnovo, A., Crupi, R., Gamba, G., Greco, G., Naseer, A., Regoli, D., et al. (2020). BeFair: Addressing Fairness in the Banking Sector. In Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp.3652-3661). New York : Institute of Electrical and Electronics Engineers Inc. [10.1109/BigData50022.2020.9377894].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/324566
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