Technical Foundations and Applications of Trustworthy AI Systems

Artificial intelligence (AI) and data-driven technologies play an increasingly decisive role in shaping outcomes across education, healthcare, finance, public services, and everyday human experiences. With this growing influence comes a corresponding responsibility. Stakeholders across domains, including data stewards, engineers, policymakers, and end users, are raising critical questions: how are models developed, tested, and validated? What data foundations underpin them? What risks, biases, and uncertainties remain? And, importantly, who is accountable at each stage of the lifecycle? Technical Foundations and Applications of Trustworthy AI Systems is grounded in the belief that transparency and trust must be treated as foundational design principles rather than retrospective considerations. This book represents a deliberate effort to integrate technical rigor with real-world applicability. Covering topics such as e-tailing, intelligent spam detection, and cryptography, this book is an excellent academic resource for graduate and doctoral students, AI engineers, machine learning practitioners, data scientists, software developers, policymakers, and more