  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.
