Trustworthy AI Systems: Engineering Secure, Scalable, and Responsible Intelligence for Real Applications

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Bol This book bridges the gap between leading-edge AI innovation and real deployment, by offering a practical guide to engineering secure, scalable, and responsible AI. Through expert insights, case studies, and technical guidance, the book empowers researchers, developers, and decision-makers to build AI that users can trust. This book bridges the gap between leading-edge AI innovation and real deployment, by offering a practical guide to engineering secure, scalable, and responsible AI. The authors describe a unified framework that merges engineering principles with ethical design, cybersecurity, explainability, and policy alignment. Through expert insights, case studies, and technical guidance, the book empowers researchers, developers, and decision-makers to build AI that users can trust.

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This book bridges the gap between leading-edge AI innovation and real deployment, by offering a practical guide to engineering secure, scalable, and responsible AI. Through expert insights, case studies, and technical guidance, the book empowers researchers, developers, and decision-makers to build AI that users can trust. This book bridges the gap between leading-edge AI innovation and real deployment, by offering a practical guide to engineering secure, scalable, and responsible AI. The authors describe a unified framework that merges engineering principles with ethical design, cybersecurity, explainability, and policy alignment. Through expert insights, case studies, and technical guidance, the book empowers researchers, developers, and decision-makers to build AI that users can trust.


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Merk Springer
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  • 9783032156051
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