http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Learn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence. The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, youll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios. The book concludes with a chapter devoted to fostering a deeper understanding of responsible AIs profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions. You will: Understand the principles of responsible AI and their importance in today's digital world Master techniques to detect and mitigate bias in AI Explore methods and tools for achieving transparency and explainability Discover best practices for privacy preservation and security in AI Gain insights into designing robust and reliable AI models.
Chapter 1: Introduction -- Chapter 2: Bias and Fairness -- Chapter 3: Transparency and Explainability -- Chapter 4: Privacy and Security -- Chapter 5: Ensuring Robustness and Reliability -- Chapter 6: Conclusion.
Avinash Manure, Shaleen Bengani, Saravanan S.
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