BeFair: Addressing Fairness in the Banking Sector
This work tackles the problem of translating fairness in ML research into practical industrial applications for the banking sector, which is an incremental step in applying existing methods.
This paper addresses algorithmic bias in the banking sector by proposing a general roadmap for fairness in ML and implementing a toolkit called BeFair. Their initial results indicate that training models without explicit constraints can exacerbate bias in predictions.
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.