Abstract
This introductory chapter presents briefly the motivation (safety, bias detection, social acceptance, etc.), applications (smart energy management, predictive maintenance, bioinformatics, e-commerce, healthcare, etc.), and challenges (accuracy/transparency trade-off, adapted to different levels of users, generalized to different application domains, etc.) of Explainable Artificial Intelligence (XAI) within the context of Digital Transformation and Cyber-Physical Systems. It overviews briefly the methods (transparent models, model-agnostic methods) used in order to explain how and why the decision was made by the model as well as the evaluation layout (user-expertise level, expressive power, computational complexity, accuracy, usefulness and relevance, coherence with prior belief, etc.) required in order to assess the quality (explainability, transparency, interpretability) of the provided explanations. It summarizes the gathered 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.
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Sayed-Mouchaweh, M. (2021). Prologue: Introduction to Explainable Artificial Intelligence. In: Sayed-Mouchaweh, M. (eds) Explainable AI Within the Digital Transformation and Cyber Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-76409-8_1
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DOI: https://doi.org/10.1007/978-3-030-76409-8_1
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