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.

翻译：在人工智能广泛融入各行各业决策过程的时代，对信任的需求从未如此突出。本文全面探讨了偏见与公平性问题，特别关注其在银行业中的影响——该领域由人工智能驱动的决策具有重大的社会后果。在此背景下，公平性、可解释性及人类监督的无缝整合至关重要，最终形成了通常所称的"负责任的人工智能"。这突显了在构建与企业文化无缝契合、同时符合人工智能法规及普遍人权标准（尤其在自动化决策系统领域）的过程中，解决偏见问题的关键性。如今，将伦理原则嵌入人工智能模型的开发、训练及部署，对于遵守即将出台的欧洲法规及促进社会福祉至关重要。本文围绕三大基本支柱展开：理解偏见、缓解偏见和解释偏见。这些研究成果通过与意大利联合圣保罗银行的合作，在实际应用场景中得以验证。这一合作不仅深化了我们对公平性的理解，还为基于人工智能的决策系统的负责任实施提供了实用工具。秉持开源原则，我们已将Bias On Demand和FairView作为可访问的Python包发布，进一步推动人工智能公平性领域的发展。