In a series of interviews, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In this latest interview, we met Bálint Gyevnár and found out about his work creating explanations for AI-based decision-making systems. I was lucky enough to study at this university for the last eight years, so I've done my undergraduate and master's here as well. I really enjoy Edinburgh – it's a very stimulating environment and there's a lot of academic discussion which prompted me to stay here.

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.

We live in a time of rapid technological change, where nearly every aspect of our lives now relies on devices that compute and connect. The resulting exponential increase in the use of cyber-physical systems has transformed industry, government, and commerce; what's more, the speed of innovation shows no signs of slowing down, particularly as the revolution in artificial intelligence (AI) stands to transform daily life even further through increasingly powerful tools for data analysis, prediction, security, and automation.1 Like past waves of extreme innovation, as this one crests, debate over ethical usage and privacy controls are likely to proliferate. So far, the intersection of AI and society has brought its own unique set of ethical challenges, some of which have been anticipated and discussed for many years, while others are just beginning to come to light. For example, academics and science fiction authors alike have long pondered the ethical implications of hyper-intelligent machines, but it's only recently that we've seen real-world problems start to surface, like social bias in automated decision-making tools, or the ethical choices made by self-driving cars.2, 5 During the past two decades, the security community has increasingly turned to AI and the power of machine learning (ML) to reap many technological benefits, but those advances have forced security practitioners to navigate a proportional number of risks and ethical dilemmas along the way. As the leader in the development of AI and ML for cybersecurity, BlackBerry Cylance is at the heart of the debate and is passionate about advancing the use of AI for good.