# Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy

## Author(s): Yang, Kaiyu; Qinami, Klint; Li, Fei-Fei; Deng, Jia; Russakovsky, Olga

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dc.contributor.authorYang, Kaiyu-
dc.contributor.authorQinami, Klint-
dc.contributor.authorLi, Fei-Fei-
dc.contributor.authorDeng, Jia-
dc.contributor.authorRussakovsky, Olga-
dc.date.accessioned2021-10-08T19:45:52Z-
dc.date.available2021-10-08T19:45:52Z-
dc.date.issued2020-01en_US
dc.identifier.citationYang, Kaiyu, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky. "Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy." Proceedings of the Conference on Fairness, Accountability, and Transparency (2020): pp. 547-558. doi:10.1145/3351095.3375709en_US
dc.identifier.urihttps://arxiv.org/pdf/1912.07726.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1df9h-
dc.description.abstractComputer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the person subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively.en_US
dc.format.extent547 - 558en_US
dc.language.isoen_USen_US
dc.relation.ispartofProceedings of the Conference on Fairness, Accountability, and Transparencyen_US
dc.rightsAuthor's manuscripten_US
dc.titleTowards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchyen_US
dc.typeConference Articleen_US
dc.identifier.doi10.1145/3351095.3375709-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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