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We investigate the behaviors that compressed convolutional models exhibit for two key areas within AI trust: (i) the ability for a model to be explained and (ii) its ability to be robust to adversarial attacks. While compression is known to shrink model size and decrease inference time, other properties of compression are not as well studied. We employ several compression methods on benchmark datasets, including ImageNet, to study how compression affects the convolutional aspects of an image model. We investigate explainability by studying how well compressed convolutional models can extract visual features with t-SNE, as well as visualizing localization ability of our models with class activation maps. We show that even with significantly compressed models, vital explainability is preserved and even enhanced. We find with applying the Carlini & Wagner attack algorithm on our compressed models, robustness is maintained and some forms of compression make attack more difficult or time-consuming.