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In this paper we investigate problematic practices and consequences of largescale vision datasets. We examine broad issues such as the question of consentand justice as well as specific concerns such as the inclusion of verifiablypornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as anexample, we perform a cross-sectional model-based quantitative census coveringfactors such as age, gender, NSFW content scoring, class-wise accuracy,human-cardinality-analysis, and the semanticity of the image class informationin order to statistically investigate the extent and subtleties of ethicaltransgressions. We then use the census to help hand-curate a look-up-table ofimages in the ImageNet-ILSVRC-2012 dataset that fall into the categories ofverifiably pornographic: shot in a non-consensual setting (up-skirt), beachvoyeuristic, and exposed private parts. We survey the landscape of harm andthreats both society broadly and individuals face due to uncritical andill-considered dataset curation practices. We then propose possible courses ofcorrection and critique the pros and cons of these. We have duly open-sourcedall of the code and the census meta-datasets generated in this endeavor for thecomputer vision community to build on. By unveiling the severity of thethreats, our hope is to motivate the constitution of mandatory InstitutionalReview Boards (IRB) for large scale dataset curation processes.

Large image datasets: A pyrrhic win for computer vision?