  We identify "values" as actions that classifiers take that speak to open
questions of significant social concern. Investigating a classifier's values
builds on studies of social bias that uncover how classifiers participate in
social processes beyond their creators' forethought. In our case, this
participation involves what counts as nutritious, what it means to be modest,
and more. Unlike AI social bias, however, a classifier's values are not
necessarily morally loathsome. Attending to image classifiers' values can
facilitate public debate and introspection about the future of society. To
substantiate these claims, we report on an extensive examination of both
ImageNet training/validation data and ImageNet-trained classifiers with custom
testing data. We identify perceptual decision boundaries in 118 categories that
address open questions in society, and through quantitative testing of rival
datasets we find that ImageNet-trained classifiers enact at least 7 values
through their perceptual decisions. To contextualize these results, we develop
a conceptual framework that integrates values, social bias, and accuracy, and
we describe a rhetorical method for identifying how context affects the values
that a classifier enacts. We also discover that classifier performance does not
straightforwardly reflect the proportions of subgroups in a training set. Our
findings bring a rich sense of the social world to ML researchers that can be
applied to other domains beyond computer vision.
