Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making

TL;DR

This paper explores bias and fairness in AI for banking, emphasizing responsible AI principles, and introduces practical tools and collaborations to promote ethical, fair decision-making aligned with regulations and human rights.

Contribution

It provides a comprehensive analysis of bias mitigation in banking AI, introduces open-source tools, and demonstrates real-world application in collaboration with a major bank.

Findings

Development of Bias On Demand and FairView Python packages

Successful application of fairness tools in real banking scenarios

Enhanced understanding of bias mitigation strategies

Abstract

In an era characterized by the pervasive integration of artificial intelligence into decision-making processes across diverse industries, the demand for trust has never been more pronounced. This thesis embarks on a comprehensive exploration of bias and fairness, with a particular emphasis on their ramifications within the banking sector, where AI-driven decisions bear substantial societal consequences. In this context, the seamless integration of fairness, explainability, and human oversight is of utmost importance, culminating in the establishment of what is commonly referred to as "Responsible AI". This emphasizes the critical nature of addressing biases within the development of a corporate culture that aligns seamlessly with both AI regulations and universal human rights standards, particularly in the realm of automated decision-making systems. Nowadays, embedding ethical principles into the development, training, and deployment of AI models is crucial for compliance with forthcoming European regulations and for promoting societal good. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. These contributions are validated through their practical application in real-world scenarios, in collaboration with Intesa Sanpaolo. This collaborative effort not only contributes to our understanding of fairness but also provides practical tools for the responsible implementation of AI-based decision-making systems. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages, further promoting progress in the field of AI fairness.