Explainable Artificial Intelligence for Sustainable Development
Advancing Social and Economic Transformations
Series: Routledge Research in Sustainability and Business;
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Product details:
- Edition number 1
- Publisher Routledge
- Date of Publication 30 September 2025
- ISBN 9781032985435
- Binding Hardback
- No. of pages314 pages
- Size 234x156 mm
- Weight 740 g
- Language English
- Illustrations 73 Illustrations, black & white; 42 Halftones, black & white; 31 Line drawings, black & white; 37 Tables, black & white 700
Categories
Short description:
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 SDGs, offering insights into its potential for responsible innovation.
MoreLong description:
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.
The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license.
MoreTable of Contents:
Preface Part 1. Foundations of Explainable Artificial Intelligence for Sustainable Development Chapter 1. The Rise, Core Principles, and Applications of Explainable Artificial Intelligence in Sustainable Development Chapter 2. Interpretable and Explainable Machine Learning: Towards Sustainable Development Goals Part 2. Explainable Artificial Intelligence in Business Decisions for Future Sustainable Solutions Chapter 3. Artificial Intelligence in Achieving Sustainable Development Goals in the Banking Sector Chapter 4. Implementing Responsible AI in Online Marketplaces for Sustainable Development Chapter 5. Explainable AI in the Attestation of Sustainability Reporting Chapter 6. Explainable Machine Learning Methods for Probability of Default in Credit Risk Modelling Chapter 7. Adding Explainability to LSTM Modeling of Business Tendency Survey Results Chapter 8. Cognitive Technologies for Explainable AI in Sustainable Decision Support Part 3. Artificial Intelligence in Societal Transformation for Future Sustainable Solutions Chapter 10. Time and Content Domain Analysis of Managerial Actions Aimed at Introducing Artificial Management Chapter 11. The Determinants of Electricity Prices Through Explainable Machine Learning Chapter 12. Household Indebtedness in the Face of Unscheduled Events: Variable Importance Analysis Chapter 13. Exploring AI Adoption in Visual Arts Education: Insights From the Polish Sector Chapter 14. Explainable AI in Psychiatry: Exploring Obstacles and Biased Credibility – A Review Chapter 15. Robotic Arm Digital Twin for Pathomorphological Diagnosis Process
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