BeFair: Addressing Fairness in the Banking Sector

TL;DR

This paper introduces BeFair, a toolkit designed to identify and mitigate bias in machine learning models within the banking industry, emphasizing the importance of fairness and providing a practical roadmap for implementation.

Contribution

It presents a general roadmap for fairness in ML and introduces the BeFair toolkit to address bias in banking sector applications.

Findings

Training without explicit constraints can worsen bias.

BeFair helps identify and mitigate bias effectively.

Initial results demonstrate the toolkit's potential benefits.

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.