Explainable Machine Learning for Scientific Insights and Discoveries

Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scient...

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Veröffentlicht in:IEEE access Jg. 8; S. 42200 - 42216
Hauptverfasser: Roscher, Ribana, Bohn, Bastian, Duarte, Marco F., Garcke, Jochen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Approximation algorithms
Biological system modeling
Data mining
Data models
Domains
Explainable machine learning
informed machine learning
interpretability
Kernel
Machine learning
Mathematical model
scientific consistency
transparency
ISSN:2169-3536, 2169-3536
Online-Zugang:Volltext
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Zusammenfassung:Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article, we review explainable machine learning in view of applications in the natural sciences and discuss three core elements that we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2976199