Convolutional neural networks (CNNs) are a key technology powering the automated technologies of seeing known as computer vision. CNNs have been especially successful in systems that perform object recognition from visual data. This article examines the persistence of a mid‐twentieth‐century ontology of the digital image in these contemporary technologies. While CNNs are multidimensional, their ontology flattens distinctions between background and foreground, between subjects and objects, and even the relations established among the categories of information used to organize and train these models. This ontology enables the introduction and amplification of bias and troubling correlations and the transfer or slippage of learned associations between humans and objects found in the training image archives. Inspecting and interpreting what CNNs learn and index through their complex architectures can be difficult if not impossible because of how they encode and obfuscate quite human ways of seeing the world and the image repertoires used to train these algorithms that are rife with residues of prior representations.

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