Explainable AI Within the Digital Transformation and Cyber Physical Systems

This book presents Explainable Artificial Intelligence (XAI), which aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that 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, that 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. This book gathers research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefitsand requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions.

• Includes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems;
• Provides a textual scientific description of the use of XAI in order to address the challenges of digital transition and cyber-physical systems;
• Presents examples and case studies in order to increase transparency and understanding of the methodological concepts.

Includes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems Provides a textual scientific description of the use of XAI in order to address the challenges of digital transition and cyber-physical systems Presents multiple examples and case studies in order to increase transparency and understanding of the methodological concepts

Auteur
Moamar Sayed-Mouchaweh received his Master degree from the University of Technology of Compiegne-France in 1999, PhD degree from the University of Reims-France in December 2002, and the Habilitation to Direct Researches (HDR) in Computer science, Control and Signal processing in December 2008. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines-Telecom Lille-Douai in France. He edited and wrote several Springer books, served as member of Editorial Board, IPC, conference, workshop and tutorial chair for different international conferences, an invited speaker, a guest editor of several special issues of international journals targeting the use of advanced artificial intelligence techniques and tools for digital transformation (energy transition and industry 4.0). He served and is serving as an expert for the evaluation of industrial and research projects in the domain of digital transformation. He is leading an inter-disciplinary and industry based research theme around the use of advanced Artificial Intelligence techniques in order to address the challenges of energy transition and Industry 4.0.

Contenu

Introduction.- Part 1 Methods used to generate explainable models.- Explainable Artificial Intelligence (XAI).- intrinsic explainable models.- model-agnostic methods.- Part 2 Evaluation layout and meaningful criteria.- expressive power.- portability evaluation layout.- accuracy evaluation layout.- algorithmic complexity.- fidelity evaluation.- stability evaluation.- representativeness evaluation layout.- local/global explanation.- Part 3 XAI applications within the context of digital transformation and cyber-physical systems.- applications of XAI in decision support tools.- smart energy management.- finance.- telemedicine and healthcare.- critical systems.- e-government.- Conclusion.