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.
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.
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
Ewa Wanda Ziemba is a Full Professor of Management at the University of Economics in Katowice, Poland, and an ordinary member of the European Academy of Sciences and Arts in Salzburg, Austria. Her research focuses on digital transformation for sustainable development, and she is internationally recognized for developing a multi-dimensional framework for a sustainable information society. She has led more than 40 research projects and currently coordinates an EU-funded initiative TOP4HoneyChains focused on developing sustainable smart honey value chains.
Wioletta Grzenda is an Associate Professor at the Institute of Statistics and Demography, Collegium of Economic Analysis at the SGH Warsaw School of Economics, Poland. She holds a PhD in Mathematics from Maria Curie-Skodowska University in Lublin, Poland, and a D.Sc. degree in economics and finance from SGH Warsaw School of Economics for her works on Bayesian modeling of family and occupational careers. She is the head of the Statistical Methods and Business Analytics Unit.
Michal Ramsza is an Associate Professor at the Institute of Mathematical Economics, Collegium of Economic Analysis at the SGH Warsaw School of Economics, Poland. He holds an M.Sc. in Mathematics from the University of Warsaw, a Ph.D., and a D.Sc. in mathematical economics from SGH Warsaw School of Economics for his works on the theory of learning in games. He is the head of the Algorithms and Applications Unit.
