UID:
almahu_9949578755102882
Umfang:
XXII, 256 p. 86 illus., 64 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2023.
ISBN:
9783031128073
Serie:
Intelligent Systems Reference Library, 232
Inhalt:
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.
Anmerkung:
Black Box Models for eXplainable Artificial Intelligence -- Fundamental Fallacies in Definitions of Explainable AI: Explainable to Whom and Why? -- An Overview of Explainable AI Methods, Forms and Frameworks.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031128066
Weitere Ausg.:
Printed edition: ISBN 9783031128080
Weitere Ausg.:
Printed edition: ISBN 9783031128097
Sprache:
Englisch
DOI:
10.1007/978-3-031-12807-3
URL:
https://doi.org/10.1007/978-3-031-12807-3
