Black-box machine learning models are used in an increasing number of high-stakes
domains, and this creates a growing need for Explainable AI (XAI). However, the use of XAI …

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …

Nowadays, systems based on machine learning (ML) are widely used in different domains.
Given their popularity, ML models have become targets for various attacks. As a result …

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness.
Several defenses have been proposed to mitigate such risks. When a defense is effective in …

The past decade has observed a great advancement in AI with deep learning-based models
being deployed in diverse scenarios including safety-critical applications. As these AI …

Interpretability, trustworthiness, and usability are key considerations in high-stake security
applications, especially when utilizing deep learning models. While these models are known …

Explanations are conceived to ensure the trustworthiness of AI systems. Yet, relying
solemnly on algorithmic solutions, as provided by explainable artificial intelligence (XAI) …

The field of artificial intelligence (AI) has experienced explosive growth in recent years, with
applications ranging from medical diagnosis to financial forecasting. However, as these …

I want to thank Dr. Stjepan Picek for his continued guidance and feedback throughout the
creation of this thesis. I also want to thank Professor George Smaragdakis and Dr. Myrthe …