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Introduction to responsible AI = implement ethical AI using Python /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Introduction to responsible AI/ by Avinash Manure, Shaleen Bengani, Saravanan S.
其他題名:	implement ethical AI using Python /
作者:	Manure, Avinash.
其他作者:	S, Saravanan.
出版者:	Berkeley, CA :Apress : : 2023.,
面頁冊數:	ix, 184 p. :illustrations, digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Python. -
電子資源:	https://doi.org/10.1007/978-1-4842-9982-1
ISBN:	9781484299821

Introduction to responsible AI = implement ethical AI using Python /
Manure, Avinash.

Introduction to responsible AIimplement ethical AI using Python /[electronic resource] :by Avinash Manure, Shaleen Bengani, Saravanan S. - Berkeley, CA :Apress :2023. - ix, 184 p. :illustrations, digital ;24 cm.

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.

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.

ISBN: 9781484299821

Standard No.: 10.1007/978-1-4842-9982-1doiSubjects--Topical Terms:

1115944
Python.

LC Class. No.: Q334.7 / .M36 2023

Dewey Class. No.: 006.301

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