Interpretable and Trustworthy AI

<p>Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. <i>Interpretable and Trustworthy AI: Techniques and Frameworks </i>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.</p><p>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.</p><p>It addresses critical challenges in AI adoption focusing on two key concerns:</p><ul> <li>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.</li> <li>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.</li> </ul><p>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.</p><p>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.</p>