Synopsis
Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. Interpretable and Trustworthy AI: Techniques and Frameworks covers key requirements for interpretability and trustworthiness of artificial intelligence (AI) models and how these needs can be met. This book explores artificial intelligence’s impact, limitations, and solutions.
It examines AI’s role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enabling automation, augmentation, and acceleration of IT-enabled business processes. The book establishes AI’s fundamental capacity to envision and implement sustainable business transformations.
It addresses critical challenges in AI adoption, focusing on two key concerns:
- AI Interpretability: Models typically optimize for accuracy but struggle to capture real-world costs, especially regarding ethics and fairness. Interpretability features help understand model learning processes, available information, and decision justifications within real-world contexts.
- Trustworthy AI: Business leaders demand responsible AI solutions that prioritize human needs, safety, and privacy. Researchers are developing methods to enhance trust in AI models and their conclusions to accelerate adoption.
Finally, the book presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to Trustworthy and Transparent AI, Explainable and Interpretable AI, Responsible AI, Generative AI, Agentic AI, and Efficient and Edge AI.
With its comprehensive structure, the book provides a comprehensive examination of AI’s potential, its current limitations, and pathways to overcome these challenges for wider adoption.
"synopsis" may belong to another edition of this title.
About the Author
Dr. Pethuru Raj is chief architect at the Edge AI Division of Reliance Jio Platforms Ltd, Bangalore, India.
Dr. Kousalya Govardhanan is a professor and dean of research-SKI at Sri Krishna College of Engineering and Technology, Coimbatore, India.
Dr. B. Sundaravadivazhagan is affiliated with the Department of Information Technology, The University of Technology and Applied Sciences-Al Mussanah, Oman.
Dr. Shubham Mahajan is an assistant professor at the Amity School of Engineering & Technology, Amity University, Haryana, India.
Dr. M. Nalini is an associate professor at the Department of Computer Science and Business Systems, S.A. Engineering College, Tamil Nadu, India.
"About this title" may belong to another edition of this title.
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Hardcover. Condition: new. Hardcover. Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. Interpretable and Trustworthy AI: Techniques and Frameworks covers key requirements for interpretability and trustworthiness of artificial intelligence (AI) models and how these needs can be met. This book explores artificial intelligences impact, limitations, and solutions.It examines AIs role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enabling automation, augmentation, and acceleration of IT-enabled business processes. The book establishes AIs fundamental capacity to envision and implement sustainable business transformations.It addresses critical challenges in AI adoption, focusing on two key concerns:AI Interpretability: Models typically optimize for accuracy but struggle to capture real-world costs, especially regarding ethics and fairness. Interpretability features help understand model learning processes, available information, and decision justifications within real-world contexts.Trustworthy AI: Business leaders demand responsible AI solutions that prioritize human needs, safety, and privacy. Researchers are developing methods to enhance trust in AI models and their conclusions to accelerate adoption.Finally, the book presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to Trustworthy and Transparent AI, Explainable and Interpretable AI, Responsible AI, Generative AI, Agentic AI, and Efficient and Edge AI.With its comprehensive structure, the book provides a comprehensive examination of AIs potential, its current limitations, and pathways to overcome these challenges for wider adoption. Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/DL) algorithms. This book covers key requirements for interpretability and trustworthiness of AI models and how these needs can be fulfilled. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781032960630
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Hardcover. Condition: new. Hardcover. Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. Interpretable and Trustworthy AI: Techniques and Frameworks covers key requirements for interpretability and trustworthiness of artificial intelligence (AI) models and how these needs can be met. This book explores artificial intelligences impact, limitations, and solutions.It examines AIs role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enabling automation, augmentation, and acceleration of IT-enabled business processes. The book establishes AIs fundamental capacity to envision and implement sustainable business transformations.It addresses critical challenges in AI adoption, focusing on two key concerns:AI Interpretability: Models typically optimize for accuracy but struggle to capture real-world costs, especially regarding ethics and fairness. Interpretability features help understand model learning processes, available information, and decision justifications within real-world contexts.Trustworthy AI: Business leaders demand responsible AI solutions that prioritize human needs, safety, and privacy. Researchers are developing methods to enhance trust in AI models and their conclusions to accelerate adoption.Finally, the book presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to Trustworthy and Transparent AI, Explainable and Interpretable AI, Responsible AI, Generative AI, Agentic AI, and Efficient and Edge AI.With its comprehensive structure, the book provides a comprehensive examination of AIs potential, its current limitations, and pathways to overcome these challenges for wider adoption. Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/DL) algorithms. This book covers key requirements for interpretability and trustworthiness of AI models and how these needs can be fulfilled. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781032960630
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