Visual Analytics for Explainable Deep Learning

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Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. This article reviews visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discusses potential challenges and future research directions.
Publisher
IEEE COMPUTER SOC
Issue Date
2018-07
Language
English
Article Type
Article
Citation

IEEE COMPUTER GRAPHICS AND APPLICATIONS, v.38, no.4, pp.84 - 92

ISSN
0272-1716
DOI
10.1109/MCG.2018.042731661
URI
http://hdl.handle.net/10203/273433
Appears in Collection
RIMS Journal Papers
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