Rajendra Gangavarapu

Mastering AI Governance (eBook, PDF)

A Guide to Building Trustworthy and Transparent AI Systems

• Format: PDF

Produktbeschreibung

This book takes you deep into the heart of the challenges and opportunities presented by the rapid evolution of Artificial Intelligence (AI) technologies. This groundbreaking book highlights the critical need for ethical, transparent, and accountable AI systems in an era of transformative innovation. Through a comprehensive exploration of real-world cases, forward-thinking strategies, and emerging governance frameworks, this book equips readers to navigate the complexities of AI governance. From addressing bias and fairness in AI systems to mitigating risks such as deepfake manipulation and data privacy violations, it provides actionable insights for policymakers, technologists, and organizations committed to fostering trust and societal benefits in AI applications.

Mastering AI Governance: A Guide to Building Trustworthy and Transparent AI Systems dives into a future where innovation thrives under the guiding principles of ethics, inclusivity, and governance excellence. Whether you are a business leader, technologist, academic, or tech-savvy AI enthusiast, this book delivers the tools and knowledge necessary to harness AI's potential responsibly.

• Offers actionable strategies for implementing ethical AI practices, ensuring fairness, transparency, and accountability;
• Provides cutting-edge insights on AI risks and delves into emerging threats and strategies to mitigate them effectively;
• Offers real-world case studies and explores global regulations and how they shape the future of ethical AI governance.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 137
• Erscheinungstermin: 26. September 2025
• Englisch
• ISBN-13: 9783031936814
• Artikelnr.: 75531457

Autorenporträt

Dr. Rajendra Prasad Gangavarapu is a distinguished Artificial intelligence (AI) thought leader with over 15 years of experience in driving transformative innovations in AI. He is recognized as one of the "100 Most Influential AI Leaders in the USA" (2024) and was awarded the prestigious AI Innovator Award at MachineCon New York (2024). Dr. Gangavarapu has consistently delivered groundbreaking AI solutions that align with organizational objectives. His leadership has propelled advancements in generative AI, machine learning frameworks, and ethical AI practices, ensuring compliance with global regulations while fostering innovation. Dr. Gangavarapu delivered guest lectures on Ethical AI and AI applications at premier institutions such as Columbia University, Georgia State University, and Mercer University. He shared his expertise at several industry forums with published research in IEEE journals and collaborations with various federal regulators on Responsible AI (RAI). Dr. Gangavarapu's work extends beyond academia and conferences, as he has led the development and deployment of enterprise-wide AI initiatives that improved credit underwriting, fraud, marketing, operational efficiency, and customer retention. His efforts have empowered organizations to scale AI capabilities, reduce costs, and achieve measurable business impact while maintaining a steadfast commitment to ethical AI governance. He holds a Doctorate in Business Administration from the Georgia State University J. Mack Robinson College of Business.

Inhaltsangabe

Foreword.- Introduction.- Navigating the AI Frontier: Emerging Trends and Governance Implications.- How to Evaluate GenAI Models: Unlocking Their Potential for Your Unique Tasks.- Taming AI Hallucinations: Innovations in Retrieval-Augmented Generation and Evaluation.- Invisible Exposure: The Privacy Risks of AI Models.- Unmasking Deepfakes: Navigating Challenges and Solutions in the Age of AI-Driven Manipulation.- Unmasking the Security Risks of Generative AI: Threats and Strategies for Defense.- Unmasking Model Bias: Building Fair, Reliable, and Trustworthy Models.- Unveiling the Black Box: Enhancing AI/ML Model Explainability for Transparency and Trust.- Model Monitoring: Lessons from Recent Failures.- Navigating Change Management: Lessons and Best Practices for Managing AI/GenAI Models in a Dynamic Landscape.- AI Governance: Preparing for the Rise of Agentic AI.- Epilogue.