This book explores the role of counterfactual reasoning in human reasoning, decision-making, memory, and creativity and reviews the logical, causal, and machine learning formalisms which have been developed to be able to use counterfactuals in intelligent systems. Questions of this sort are fundamental to counterfactual reasoning, the capacity to imagine and understand alternatives and their implications. From a psychological perspective, counterfactual thinking has been acknowledged as a key cognitive process in human cognition for a long time, and it has recently gained significant importance in the fields of artificial intelligence, explainable machine learning, and decision support systems.

In this book, researchers and practitioners from across the fields of artificial intelligence, cognitive psychology, and philosophy discuss the diverse aspects of this intriguing concept, from its psychological and philosophical roots to its contemporary computational implementations.

It highlights applications of counterfactual explanations, including explainable AI, visual counterfactual generation, and medical image analysis, showing the potential of CFs for increasing transparency, trust, and accountability in high-stakes areas like medicine.

The book’s theoretical approach is directly linked to practice, making it useful for researchers, graduate students and AI, ML, computer vision, medical imaging, cognitive science, and related professionals. It gives an introduction as well as future trends in one of the fastest growing fields of explainable and trustworthy AI.

Ridhi Arora (Member, IEEE) is Assistant Professor at School of Computer Science and Engineering, Manipal University Jaipur, India. She was previously Postdoctoral Associate at the University of Pittsburgh, USA, working in the Department of Radiology, School of Medicine, where she aimed to design explainable artificial intelligence (XAI) models for breast cancer detection and classification from mammographic X-rays and digital breast tomosynthesis (DBT).

She received the Ph.D. degree in Computer Science from the Indian Institute of Technology Roorkee (IIT Roorkee), India. Her research focuses on medical image analysis, machine learning, deep learning, explainable artificial intelligence, counterfactual reasoning, and computer-aided diagnosis for medical use.

She has reviewed papers for a number of international journals and is Member of several professional societies such as IEEE, SPIE, and American Association of Physicists in Medicine (AAPM). She is currently working on the development of interpretable, clinically actionable, and trustworthy AI systems for health care.

Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Professor with the Techno International New Town, Kolkata, and Visiting Fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored and 70 edited books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD), etc. He has delivered more than 160 keynote addresses across 18 countries.