 While these general points may explain why deep convolutional nets tend to succeed on a wide variety of tasks, their complex structure makes it difficult to explain their decisions in specific cases. This concern interfaces with the XAI (explainable AI) movement, which aims to inspire the development of better tools to analyze the decisions of computer algorithms, especially so that AI systems can be certified to meet practical or legal requirements  (Explainable Artificial Intelligence (XAI); B. Goodman &amp; Flaxman 2017). Deep Visualization methods are important tools in addressing these goals for deep neural networks. One popular family of methods uses further machine learning to create an artificial image that maximizes the activation of some particular hidden layer unit (Yosinski et al. 2015). The image is intended to give one an impression of the kind of feature that unit detects when it fires. As expected, the images look more complex and more object-like as we ascend the level hierarchy (for examples and software, see http://yosinski.com/deepvis). Without additional processing, however, many of these visualizations appear chimerical and nonsensical, and it is not clear exactly how well this method reveals features that are genuinely important in the network’s processing. Another family of methods attempts to reveal the aspects of input images that are most salient for the nets’ decision-making. Relevance decomposition, for example, determines which nodes, if deactivated, would have had the greatest effect on some particular decision (Montavon, Samek, &amp; Müller 2018). This can generate a “heatmap”, which shows the aspects of the input that were most influential in that decision. Further machine learning has also been used to build systems able to provide brief English phrases describing the features that lead to a net’s decisions (Hendricks et al. 2016   [OIR];  Ehsan et al. 2018). Despite these advances, the methodologies needed for an adequate explanation of a deep network’s behavior remain unclear and would benefit from further philosophical reflection (Lipton 2016 [OIR]; Zednik 2019 [OIR]).