The three-year project, titled Fair Learning, will address the main ethical, technical and regulatory challenges posed by the growing automation of decisions that impact society. A multidisciplinary team of BBVA and The University of Navarra will explore the use of advanced mathematical and statistical methods to correct data bias, integrate philosophical and ethical frameworks into model development, and devise a set of best practices based on international legislation.
With decision-making processes becoming increasingly automated, including loan approvals, recruitment or access to services, we must ensure that artificial intelligence systems are both fair and transparent. These challenges are not only technical but also social, and therefore require ethical reflection, oversight and institutional commitment.
The Fair Learning project, coordinated by BBVA’s own Data University and the Institute of Data Science and Artificial Intelligence (DATAI) at the University of Navarra, aims to address this challenge from technological, philosophical and legal standpoints.
Over the next three years, twenty experts—including BBVA data scientists and University of Navarra academics from various disciplines such as engineering, philosophy, medicine, law and economics—will work together to design a framework for detecting, mitigating and correcting AI bias, guaranteeing privacy, fairness and individual autonomy, and for defining best practices in line with current law and regulations.
“This is a genuinely groundbreaking project in the financial sector and a further show of BBVA’s commitment to advancing its technological transformation without losing focus on people,” remarked Josep Amorós, project coordinator and Senior Manager of Analytics Transformation at BBVA, adding that “AI is already a central pillar of the bank’s transformation and will only gain in prominence over the coming years. Therefore, we must ensure its development adheres to principles of fairness, responsibility and transparency.”
According to Jesús López Fidalgo, project coordinator for the University of Navarra team and head of the Institute of Data Science and AI, “responsible AI is one of the great challenges of this new era, and beyond the mere application of general ethical principles lies an open field to explore through multidisciplinary research. We excel at this type of research at the University of Navarra, which has the optimal human resources to carry it out, working alongside the team at BBVA’s Data University.”
The virtuous triangle: technology, philosophy and regulation
One of Fair Learning’s main avenues of research is how to mitigate the presence of bias in data. To succeed in this task, the research team will utilize advanced mathematical and statistical methods to help prevent models from learning discriminatory or biased patterns while being trained, compensate for the under-representation of certain datasets (e.g. a specific demographic group) in larger ones, and monitor the results and outputs of trained models to ensure that they do not infringe the interests of any population group.
The team will also integrate philosophical frameworks such as human-centered AI and virtue ethics, which guide technological development based on moral criteria and social responsibility. The aim is to build systems that respect privacy, promote fairness and reinforce human autonomy without replacing it. The project will also examine how best to conduct ethical audits and establish ongoing assessment processes to ensure the continuous improvement of such systems.
In the regulatory realm, the Fair Learning project will analyze the existing legal framework, which is built around the new European Union AI Regulation (EU AI Act), to define best practices and propose recommendations to ensure ethical and legally aligned implementation of AI systems.
Fair Learning is part of a wider ongoing partnership between BBVA and the University of Navarra in the field of data. This relationship began in 2020 with a pioneering agreement to train the bank’s employees in data science, create a dedicated track for BBVA employees in the official Master’s in Big Data Science, and promote joint industrial PhDs between the bank and the university.
To date, more than 90 students have graduated from the master’s programme, 135 data scientists have been certified, and 12 PhD students are currently enrolled, bringing the number of BBVA professionals specialized as a result of the alliance to 239. In 2024, this project reached the executive level with a Senior Management Program in Generative AI, which has already trained upward of 150 BBVA leaders in the strategic use of these technologies.
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