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Recently, deep learning has been advancing the state of the art in artificialintelligence to a new level, and humans rely on artificial intelligencetechniques more than ever. However, even with such unprecedented advancements,the lack of explanation regarding the decisions made by deep learning modelsand absence of control over their internal processes act as major drawbacks incritical decision-making processes, such as precision medicine and lawenforcement. In response, efforts are being made to make deep learninginterpretable and controllable by humans. In this paper, we review visualanalytics, information visualization, and machine learning perspectivesrelevant to this aim, and discuss potential challenges and future researchdirections.

Visual Analytics for Explainable Deep Learning