Explainable AI Within the Digital Transformation and Cyber Physical Systems: XAI Methods and Applications
- Length: 208 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2021-12-01
- ISBN-10: 3030764087
- ISBN-13: 9783030764081
- Sales Rank: #0 (See Top 100 Books)
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.
Cover Front Matter 1. Prologue: Introduction to Explainable Artificial Intelligence 2. Principles of Explainable Artificial Intelligence 3. Science of Data: A New Ladder for Causation 4. Explainable Artificial Intelligence for Predictive Analytics on Customer Turnover: A User-Friendly Interface for Non-expert Users 5. An Efficient Explainable Artificial Intelligence Model of Automatically Generated Summaries Evaluation: A Use Case of Bridging Cognitive Psychology and Computational Linguistics 6. On the Transparent Predictive Models for Ecological Momentary Assessment Data 7. Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Study 8. A Critical Study on the Importance of Feature Selection for Diagnosing Cyber-Attacks in Water Critical Infrastructures 9. A Study on the Effect of Dimensionality Reduction on Cyber-Attack Identification in Water Storage Tank SCADA Systems Back Matter
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