Explainable AI Within the Digital Transformation and Cyber Physical Systems
XAI Methods and Applications
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Beschreibung
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 benefits and 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.
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
Autor*in
Moamar Sayed-Mouchaweh
Themen in »Explainable AI Within the Digital Transformation and Cyber Physical Systems«
Explainable Artificial Intelligence (XAI) Digital Transformation and Cyber Physical Systems XAI applications explainable models in XAI Linear regression Logistic regression Decision trees