BeFair: Addressing Fairness in the Banking Sector

2020/12/10 by Alessandro Castelnovo, Riccardo Crupi, Giulia Del Gamba +5
Computer Science · Social Sciences · #Business #Computer science #Data science #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #Field (mathematics) #Privacy-Preserving Technologies in Data #Risk analysis (engineering) #cs.CY #cs.LG

paper · pdf · doi:10.1109/bigdata50022.2020.9377894

published as 2020 IEEE International Conference on Big Data (Big Data) · 6 pages, 3 figures

openalex publication_date 2020/12/10 · arxiv created 2021/02/04 · arxiv updated 2021/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

Citations

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