Overview
- Written for beginners and advanced machine learning users, including engineers and researchers on AI and applications
- Covers concepts such as black box models, transparency, interpretable machine learning and explanations
- Presents evaluation methods and metrics, ethical, legal, and social issues, and applications and examples of XAI
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 232)
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Table of contents (11 chapters)
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Front Matter
Keywords
- Intelligent Systems
- Artificial Intelligence
- Explainable AI
- Neural Networks
- Deep Learning
- Applied Machine Learning
- Interpretable Learning
- Autonomous Systems
- Generative Adversarial Learning
- Domain Adaptation
- Network Interpretability
- Class Imbalance
- Data Augmentation
- Semi Supervised Learning
- Pattern Recognition
- Noise Reduction
- Imitation Learning
- Image Segmentation
- Image Completion
- Reinforcement Learning
About this book
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.
The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Editors and Affiliations
Bibliographic Information
Book Title: Explainable AI: Foundations, Methodologies and Applications
Editors: Mayuri Mehta, Vasile Palade , Indranath Chatterjee
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-031-12807-3
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-12806-6Published: 20 October 2022
Softcover ISBN: 978-3-031-12809-7Published: 21 October 2023
eBook ISBN: 978-3-031-12807-3Published: 19 October 2022
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XXII, 256
Number of Illustrations: 22 b/w illustrations, 64 illustrations in colour
Topics: Computational Intelligence, Machine Learning, Artificial Intelligence