Visual Analytics for Explainable Deep Learning

Jaegul Choo, Shixia Liu

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)84-92
Number of pages9
JournalIEEE Computer Graphics and Applications
Volume38
Issue number4
DOIs
Publication statusPublished - 2018 Jul 1

Keywords

  • computer graphics
  • deep learning
  • explainable deep learning
  • interactive visualization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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