Interpretable and Trustworthy AI
Techniques and Frameworks
Herausgeber: Sundaravadivazhagan, B.; Mahajan, Shubham; Raj, Pethuru; Nalini, M.; Govardhanan, Kousalya
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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 AI models and how these needs can be met. This book is structured in three main sections exploring artificial intelligence's impact, limitations, and solutions. The first section examines AI's role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enablin...
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 AI models and how these needs can be met. This book is structured in three main sections exploring artificial intelligence's impact, limitations, and solutions. The first section 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 section establishes AI's fundamental capacity to envision and implement sustainable business transformations. The second section 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. The final section 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.
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