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