Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
21.06.2026
Verlag
Independently publishedSeitenzahl
360
Maße (L/B/H)
27,9/21,6/1,9 cm
Gewicht
836 g
Sprache
Englisch
EAN
9798183567755
Responsible AI: A Practical Guide to Building Ethical, Secure, and Trustworthy AI Systems
Artificial Intelligence is transforming every industry, but building AI systems that are ethical, secure, transparent, and trustworthy has become one of the most important challenges facing organizations today.
Responsible AI: A Practical Guide to Building Ethical, Secure, and Trustworthy AI Systems provides a practical roadmap for designing, developing, deploying, governing, and monitoring AI systems responsibly throughout their lifecycle. Whether you are working with machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI agents, this book equips you with the knowledge and tools needed to manage risks while maximizing business value.
In this book, you will learn how to:
Apply Responsible AI principles, ethics, and human-centered design
Identify and mitigate bias while improving fairness
Implement explainability and transparency techniques
Protect privacy and comply with regulations such as GDPR and CCPA
Secure AI systems against adversarial attacks, prompt injection, jailbreaking, model extraction, and data leakage
Build safe, reliable, and trustworthy AI applications
Establish AI governance frameworks and risk management programs
Navigate the evolving regulatory landscape, including the EU AI Act and NIST AI RMF
Deploy Generative AI, LLMs, and AI agents responsibly
Implement content moderation, safety controls, and human oversight
Test, validate, monitor, and audit AI systems throughout their lifecycle
Apply Responsible AI practices across healthcare, financial services, government, and enterprise environments
The book combines foundational concepts with practical implementation guidance, real-world case studies, architecture patterns, governance frameworks, compliance strategies, and hands-on labs covering bias detection, fairness evaluation, explainable AI, LLM safety testing, AI security assessments, and governance documentation.
You'll also gain access to Responsible AI checklists, governance templates, risk assessment worksheets, model card templates, datasheet templates, and other practical resources that can be applied directly within your organization.
This book is ideal for AI and Machine Learning Engineers, Data Scientists, Software Developers, Architects, Security Professionals, Risk and Compliance Teams, Privacy Officers, Product Managers, Business Leaders, Researchers, Students, and anyone interested in building trustworthy AI systems.
As AI adoption accelerates, Responsible AI is no longer optional-it is essential. This guide provides the practical frameworks, controls, and best practices needed to build AI systems that are fair, explainable, secure, compliant, and worthy of trust.
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