  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. In this paper, we review visual
analytics, information visualization, and machine learning perspectives
relevant to this aim, and discuss potential challenges and future research
directions.
