ISBN: 9798260014332 - technical foundations and applications of trustworthy ai systems (11 resultados)

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Condición: New.

Hardback. Condición: New.

Hardback. Condición: New.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condición: new. Hardcover. 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 This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…