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Introduction to Responsible AI : Implement Ethical AI Using Python / by Avinash Manure, Shaleen Bengani, Saravanan S.

Knovel General Engineering & Project Administration Academic Available online

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O'Reilly Online Learning: Academic/Public Library Edition Available online

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Format:
Book
Author/Creator:
Manure, Avinash.
Contributor:
Bengani, Shaleen.
S, Saravanan.
Language:
English
Subjects (All):
Artificial intelligence.
Machine learning.
Python (Computer program language).
Artificial Intelligence.
Machine Learning.
Python.
Local Subjects:
Artificial Intelligence.
Machine Learning.
Python.
Physical Description:
1 online resource (192 pages)
Edition:
1st ed. 2023.
Place of Publication:
Berkeley, CA : Apress : Imprint: Apress, 2023.
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, you’ll 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 AI’s 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.
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.
Notes:
Includes index.
ISBN:
9781484299821
1484299825
OCLC:
1410594402

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