Introduction to responsible AI : implement ethical AI using Python / Avinash Manure, Shaleen Bengani, Saravanan S.
2023
Q334.7 .M36 2023
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Title
Introduction to responsible AI : implement ethical AI using Python / Avinash Manure, Shaleen Bengani, Saravanan S.
Author
ISBN
9781484299821 (electronic bk.)
1484299825 (electronic bk.)
1484299817
9781484299814
1484299825 (electronic bk.)
1484299817
9781484299814
Published
[California] : Apress, [2023]
Language
English
Description
1 online resource
Item Number
10.1007/978-1-4842-9982-1 doi
Call Number
Q334.7 .M36 2023
Dewey Decimal Classification
006.301
Summary
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.
Note
Includes index.
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Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on December 08, 2023).
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Print version: 9781484299814
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Table of Contents
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.
Chapter 2: Bias and Fairness
Chapter 3: Transparency and Explainability
Chapter 4: Privacy and Security
Chapter 5: Ensuring Robustness and Reliability
Chapter 6: Conclusion.