Explainable Artificial Intelligence For Sustainable Development
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Explainable AI (XAI) for Sustainable Development
This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain. • Focuses on virtual machine placement and migration techniques for cloud data centres • Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services • Includes application of placement techniques for quality of service, performance, and reliability improvement • Explores data centre resource management, load balancing and orchestration using machine learning techniques • Analyses dynamic and scalable resource scheduling with a focus on resource management The reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.
Explainable AI (Xai) for Sustainable Development
"This book presents innovative research works to automate, innovate, design, and deploy AI for Real-World Applications. It discussed AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain. Focuses on Virtual machine placement and migration techniques for cloud data centres Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services Includes application of placement techniques for quality of service, performance, and reliability improvement Explores data centre resource management, load balancing and orchestration using machine learning techniques Analyses Dynamic and scalable resource scheduling with a focus on resource management The reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology"--
Explainable Artificial Intelligence for Sustainable Development
This book explores how transparent, interpretable AI technologies can support sustainable progress across industries and societies. It brings together theoretical foundations and practical applications of explainable AI (XAI) aligned with the UN's Sustainable Development Goals (SDGs), offering insights into its potential for responsible innovation. It provides a comprehensive understanding of how explainable AI enhances trust, ethics, and accountability in AI-driven decisions. Through diverse case studies -- from banking, e-commerce, and sustainability reporting, to psychiatry, education, and energy--the book demonstrates XAI's transformative role in driving sustainable business practices and societal well-being. Each chapter merges cutting-edge research with real-world examples, making complex AI systems more accessible and socially relevant. The book bridges gaps between disciplines, offering a holistic and actionable perspective on AI for sustainability. This book is a vital resource for researchers, professionals, and policymakers seeking to harness AI responsibly. Academics in social sciences, economics, and information systems will find a strong theoretical base, while practitioners in business, government, and NGOs gain practical tools for implementing XAI in real contexts. It is also well-suited for students, educators, and AI enthusiasts aiming to align innovation with sustainable, ethical transformation.