Introduction to responsible AI : implement ethical AI using Python
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Format
Language
English
ISBN
9781484299821, 1484299825
UPC
10.1007/978-1-4842-9982-1
Notes
General Note
Includes index.
Description
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.
Local note
O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Manure, A., Bengani, S., & S, S. (2023). Introduction to responsible AI: implement ethical AI using Python . Apress.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Manure, Avinash, Shaleen, Bengani and Saravanan, S. 2023. Introduction to Responsible AI: Implement Ethical AI Using Python. [California]: Apress.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Manure, Avinash, Shaleen, Bengani and Saravanan, S. Introduction to Responsible AI: Implement Ethical AI Using Python [California]: Apress, 2023.
Harvard Citation (style guide)Manure, A., Bengani, S. and S, S. (2023). Introduction to responsible AI: implement ethical AI using python. [California]: Apress.
MLA Citation, 9th Edition (style guide)Manure, Avinash,, Shaleen Bengani, and Saravanan S. Introduction to Responsible AI: Implement Ethical AI Using Python Apress, 2023.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
7abf4c36-4d16-5720-6dbc-0a22b343367a-eng
Grouping Information
| Grouped Work ID | 7abf4c36-4d16-5720-6dbc-0a22b343367a-eng |
|---|---|
| Full title | introduction to responsible ai implement ethical ai using python |
| Author | manure avinash |
| Grouping Category | book |
| Last Update | 2024-12-17 08:40:50AM |
| Last Indexed | 2024-12-17 08:46:06AM |
Book Cover Information
| Image Source | default |
|---|---|
| First Loaded | Dec 22, 2024 |
| Last Used | Dec 22, 2024 |
Marc Record
| First Detected | Dec 16, 2024 11:27:26 PM |
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| Last File Modification Time | Dec 17, 2024 08:26:50 AM |
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MARC Record
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| 245 | 1 | 0 | |a Introduction to responsible AI :|b implement ethical AI using Python /|c Avinash Manure, Shaleen Bengani, Saravanan S. |
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| 500 | |a Includes index. | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on December 08, 2023). | ||
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| 700 | 1 | |a Bengani, Shaleen,|e author. | |
| 700 | 1 | |a S, Saravanan,|e author. | |
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