mlsanityData to 7 Oct 2026

Large image datasets: A pyrrhic win for computer vision?

arXiv 2006.16923 ↗First posted 24 Jun 2020cs.CY stat.AP stat.MLcited by 237

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Abstract (arXiv)

In this paper we investigate problematic practices and consequences of large scale vision datasets. We examine broad issues such as the question of consent and justice as well as specific concerns such as the inclusion of verifiably pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an example, we perform a cross-sectional model-based quantitative census covering factors such as age, gender, NSFW content scoring, class-wise accuracy, human-cardinality-analysis, and the semanticity of the image class information in order to statistically investigate the extent and subtleties of ethical transgressions. We then use the census to help hand-curate a look-up-table of images in the ImageNet-ILSVRC-2012 dataset that fall into the categories of verifiably pornographic: shot in a non-consensual setting (up-skirt), beach voyeuristic, and exposed private parts. We survey the landscape of harm and threats both society broadly and individuals face due to uncritical and ill-considered dataset curation practices. We then propose possible courses of correction and critique the pros and cons of these. We have duly open-sourced all of the code and the census meta-datasets generated in this endeavor for the computer vision community to build on. By unveiling the severity of the threats, our hope is to motivate the constitution of mandatory Institutional Review Boards (IRB) for large scale dataset curation processes.

Key facts. Cited by 237 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2020.

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Citing papers per year: 2021: 44; 2022: 48; 2023: 58; 2024: 46; 2025: 22; 2026: 19.

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