Moamar Sayed-Mouchaweh - Explainable AI Within the Digital Transformation and Cyber Physical Systems: XAI Methods and Applications

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Explainable Artificial Intelligence (XAI) aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The produced explanations allow to reveal how the model functions, why it behaves that way in the past, present, and future, why certain actions were taken or must be taken, how certain goals can be achieved, how the system reacts to certain inputs or actions, what are the causes for the occurrence of a certain fault and how this occurrence can be avoided in the future, etc. The need for explanations is increasingly becoming necessary in multiple application domains, such as smart grids, autonomous cars, smart factory or industry 4.0, telemedicine and healthcare, etc., in particular within the context of digital transformation and cyber-physical systems.

This book gathers research contributions aiming at the development and/or the use of XAI techniques, in particular within the context of digital transformation and cyber-physical systems. This book aims to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization.

The discussed methods and techniques cover different kinds of designed explainable models (transparent models, model agnostic methods), evaluation layout and criteria (user-expertise level, expressive power, portability, computational complexity, accuracy, etc.), and major applications (energy, industry 4.0, critical systems, telemedicine, finance, e-government, etc.). The goal is to provide readers with an overview of advantages and limits of the generated explainable models in different application domains. This allows to highlight the benefits and requirements of using explainable models in different application domains and to provide guidance to readers to select the most adapted models to their specified problem and conditions.

Making machine learning-based AI explainable faces several challenges. Firstly, the explanations must be adapted to different stakeholders (end users, policy makers, industries, utilities, etc.) with different levels of technical knowledge (managers, engineers, technicians, etc.) in different application domains. Secondly, it is important to develop an evaluation framework and standards in order to measure the effectiveness of the provided explanations at the human and the technical levels. For instance, this evaluation framework must be able to verify that each explanation is consistent across similar predictions (similar observations) over time, is expressive in order to increase the user confidence (trust) in the decisions made, promote impartial and fair decisions, and improve the user task performance.

Finally, the editor is very grateful to all authors and reviewers for their valuable contribution. He would like also to acknowledge Mrs. Mary E. James for establishing the contract with Springer and supporting the editor in any organizational aspects. The editor hopes that this book will be a useful basis for further fruitful investigations for researchers and engineers as well as a motivation and inspiration for newcomers in order to address the challenges related to energy transition.