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Computer vision technology is being used by many but remains representative of only a few.
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No classification without representation: Assessing geodiversity issues in open data sets for the developing world. In NeurIPS workshop: Machine Learning for the Developing World
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Microsoft COCO: Common objects in context. In European conference on computer vision
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Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 259–268
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Ross Girshick. 2015 · 2015
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Valence, arousal, familiarity, concreteness, and imageability ratings for 292 two-character Chinese nouns in Cantonese speakers in Hong Kong
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP
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Places: A 10 million Image Database for Scene Recognition
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Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency . 77–91
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Women also snowboard: Overcoming bias in captioning models. In European Conference on Computer Vision
Kaylee Burns, Lisa Anne Hendricks, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018 · 2018
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Demographics and dynamics of mechanical Turk workers. In Proceedings of the eleventh acm international conference on web search and data mining . ACM, 135–143
Djellel Difallah, Elena Filatova, and Panos Ipeirotis. 2018 · 2018
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Datasheets for Datasets. In Workshop on Fairness, Accountability, and Transparency in Machine Learning
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, and Kate Crawford. 2018 · 2018
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Women also snowboard: Overcoming bias in captioning models. In European Conference on Computer Vision . Springer, 793–811
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018 · 2018
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Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, et al · 2018
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Learning adversarially fair and transferable representations
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Model Cards for Model Reporting. In ACM Conference on Fairness, Accountability and Transparency
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Algorithms of oppression: How search engines reinforce racism
Safiya Umoja Noble. 2018 · 2018
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Racial Influence on Automated Perceptions of Emotions
Lauren Rhue. 2018 · 2018
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Inclusivefacenet: Improving face attribute detection with race and gender diversity. In Proceedings of FATML
Hee Jung Ryu, Hartwig Adam, and Margaret Mitchell. 2018 · 2018
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Convnets and ImageNet beyond accuracy: Understanding mistakes and uncovering biases. In Proceedings of the European Conference on Computer Vision (ECCV) . 498–512
Pierre Stock and Moustapha Cisse. 2018 · 2018
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Deep neural networks are more accurate than humans at detecting sexual orientation from facial images
Yilun Wang and Michal Kosinski. 2018 · 2018
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AI Now Report 2018
Meredith Whittaker, Kate Crawford, Roel Dobbe, Genevieve Fried, Elizabeth Kaziunas, Varoon Mathur, Sarah Myers West, Rashida Richardson, Jason Schultz, and Oscar Schwartz. 2018 · 2018
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Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society . ACM, 335–340
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
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Implicit Diversity in Image Summarization
L Elisa Celis and Vijay Keswani. 2019 · 2019
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Does Object Recognition Work for Everyone?
Terrance DeVries, Ishan Misra, Changhan Wang, and Laurens van der Maaten. 2019 · 2019
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Chris Dulhanty and Alexander Wong. 2019 · 2019
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50 Years of Test (Un)fairness: Lessons for Machine Learning. In ACM Conference on Fairness, Accountability and Transparency
Ben Hutchinson and Margaret Mitchell. 2019 · 2019
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Large-Scale Long-Tailed Recognition in an Open World. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2537–2546
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu. 2019 · 2019
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