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Sinopsis
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
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Acerca del autor
Dr.-Ing. Otmane Azeroual is a world-renowned senior researcher, lecturer, and thought leader in digital transformation, artificial intelligence, and advanced data-driven research infrastructures. His groundbreaking interdisciplinary work at the nexus of computer science, higher education research, innovation management, and industry has set new standards in how data and AI reshape science and society. His research on the design, deployment, and critical evaluation of Current Research Information Systems (CRIS) -- combined with cutting-edge AI-powered analytics -- is internationally acclaimed and among the most cited in the field. Dr. Azeroual's pioneering contributions drive the evolution of intelligent data ecosystems that empower Open Science, elevate transparency, and revolutionize strategic decision-making in science policy worldwide. Holding a Ph.D. in Engineering Informatics and a robust foundation in business information systems and software engineering, he has authored numerous high-impact publications in top-tier journals and has been a sought-after keynote and plenary speaker at prestigious global conferences. His expertise is recognized by editorial boards and scientific committees internationally, where he shapes the future agenda of research data science. Bridging academia and private industry, Dr. Azeroual uniquely fuses rigorous theoretical insight with practical innovation. His leadership in both sectors fuels transformative digital strategies and sustainable technological solutions. As an inspiring educator, he equips emerging researchers and professionals with cutting-edge knowledge in AI, data science, research analytics, and project management -- preparing them to excel in complex, dynamic environments. Passionate about harnessing AI for evidence-based governance, Dr. Azeroual actively collaborates across disciplines to build resilient, transparent, and data-driven research infrastructures that set global benchmarks. His visionary work not only advances scientific understanding but also catalyzes paradigm shifts that benefit institutions, governments, and society at large.
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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 multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798260014332
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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. Nº de ref. del artículo: 9798260014332
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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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9798260014332
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Buch. Condición: Neu. Technical Foundations and Applications of Trustworthy AI Systems | Otmane Azeroual | Buch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798260014332 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 135210061
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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. Nº de ref. del artículo: 9798260014332
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