Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

With the broader and highly successful usage of machine learning (ML) in industry and the sciences, there has been a growing demand for explainable artificial intelligence (XAI). Interpretability and explanation methods for gaining a better understanding of the problem-solving abilities and strategi...

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Veröffentlicht in:	Proceedings of the IEEE Jg. 109; H. 3; S. 247 - 278
Hauptverfasser:	Samek, Wojciech, Montavon, Gregoire, Lapuschkin, Sebastian, Anders, Christopher J., Muller, Klaus-Robert
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial intelligence Artificial neural networks Best practice Best practices Black-box models Deep learning Explainable artificial intelligence explainable artificial intelligence (XAI) Interpretability Machine learning model transparency Neural networks Problem-solving Systematics Unsupervised learning
ISSN:	0018-9219, 1558-2256
Online-Zugang:	Volltext
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