{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:55:43Z","timestamp":1782280543991,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":81,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T00:00:00Z","timestamp":1580083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100006445","name":"Center for Hierarchical Manufacturing, National Science Foundation","doi-asserted-by":"publisher","award":["1763642"],"award-info":[{"award-number":["1763642"]}],"id":[{"id":"10.13039\/100006445","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,1,27]]},"DOI":"10.1145\/3351095.3375709","type":"proceedings-article","created":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T21:36:21Z","timestamp":1607463381000},"page":"547-558","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":219,"title":["Towards fairer datasets"],"prefix":"10.1145","author":[{"given":"Kaiyu","family":"Yang","sequence":"first","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klint","family":"Qinami","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Fei-Fei","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Deng","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Russakovsky","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,1,27]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"101st Congress, 2nd Session. 101 H.R. 2273. 1990","author":"House U.S.","year":"1990"},{"key":"e_1_3_2_2_2_1","volume-title":"88th Congress, 1st Session. 88 H.R. 6060. 1963","author":"House U.S.","year":"1963"},{"key":"e_1_3_2_2_3_1","volume-title":"98th Congress, 2nd Session. 98 H.R. 5490. 1984","author":"House U.S.","year":"1984"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1075\/jlp.15.3.08aie"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.3758\/BF03200738"},{"key":"e_1_3_2_2_6_1","unstructured":"Solon Barocas Elizabeth Bradley Vasant Honavar and Foster Provost. 2017. Big data data science and civil rights. arXiv preprint arXiv:1706.03102 (2017).  Solon Barocas Elizabeth Bradley Vasant Honavar and Foster Provost. 2017. Big data data science and civil rights. arXiv preprint arXiv:1706.03102 (2017)."},{"key":"e_1_3_2_2_7_1","first-page":"671","article-title":"Big data's disparate impact","volume":"104","author":"Barocas Solon","year":"2016","journal-title":"Calif. L. Rev."},{"key":"e_1_3_2_2_8_1","unstructured":"Kristy Beers F\u00e4gersten. 2007. A sociolinguistic analysis of swear word offensiveness. Universit\u00e4t des Saarlands.  Kristy Beers F\u00e4gersten. 2007. A sociolinguistic analysis of swear word offensiveness. Universit\u00e4t des Saarlands."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.3758\/BF03195349"},{"key":"e_1_3_2_2_10_1","volume-title":"Conference on fairness, accountability and transparency. 77--91","author":"Buolamwini Joy","year":"2018"},{"key":"e_1_3_2_2_11_1","volume-title":"European Conference on Computer Vision.","author":"Burns Kaylee","year":"2018"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_2_2_14_1","unstructured":"L Elisa Celis and Vijay Keswani. 2019. Implicit Diversity in Image Summarization. arXiv preprint arXiv:1901.10265 (2019).  L Elisa Celis and Vijay Keswani. 2019. Implicit Diversity in Image Summarization. arXiv preprint arXiv:1901.10265 (2019)."},{"key":"e_1_3_2_2_15_1","volume-title":"Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012","author":"Chang Angel X","year":"2015"},{"key":"e_1_3_2_2_16_1","volume-title":"International journal of cosmetic science 13, 4","author":"Chardon A","year":"1991"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.3758\/BF03195585"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_2_19_1","unstructured":"Terrance DeVries Ishan Misra Changhan Wang and Laurens van der Maaten. 2019. Does Object Recognition Work for Everyone? arXiv preprint arXiv:1906.02659 (2019).  Terrance DeVries Ishan Misra Changhan Wang and Laurens van der Maaten. 2019. Does Object Recognition Work for Everyone? arXiv preprint arXiv:1906.02659 (2019)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pragma.2016.01.009"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159661"},{"key":"e_1_3_2_2_22_1","unstructured":"Chris Dulhanty and Alexander Wong. 2019. Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets. arXiv preprint arXiv:1905.01347 (2019).  Chris Dulhanty and Alexander Wong. 2019. Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets. arXiv preprint arXiv:1905.01347 (2019)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_2_24_1","unstructured":"Harrison Edwards and Amos Storkey. 2016. Censoring representations with an adversary. In ICLR.  Harrison Edwards and Amos Storkey. 2016. Censoring representations with an adversary. In ICLR."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2359646"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Thomas B Fitzpatrick. 1988. The validity and practicality of sun-reactive skin types I through VI. Archives of dermatology 124 6 (1988) 869--871.  Thomas B Fitzpatrick. 1988. The validity and practicality of sun-reactive skin types I through VI. Archives of dermatology 124 6 (1988) 869--871.","DOI":"10.1001\/archderm.124.6.869"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1177\/016344301023005005"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1177\/146954050200200202"},{"key":"e_1_3_2_2_31_1","volume-title":"Datasheets for Datasets. In Workshop on Fairness, Accountability, and Transparency in Machine Learning.","author":"Gebru Timnit","year":"2018"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Ken J Gilhooly and Robert H Logie. 1980. Age-of-acquisition imagery concreteness familiarity and ambiguity measures for 1 944 words. Behavior research methods & instrumentation 12 4 (1980) 395--427.  Ken J Gilhooly and Robert H Logie. 1980. Age-of-acquisition imagery concreteness familiarity and ambiguity measures for 1 944 words. Behavior research methods & instrumentation 12 4 (1980) 395--427.","DOI":"10.3758\/BF03201693"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_3_2_2_34_1","unstructured":"Moritz Hardt Eric Price Nati Srebro etal 2016. Equality of opportunity in supervised learning. In Advances in neural information processing systems. 3315--3323.  Moritz Hardt Eric Price Nati Srebro et al. 2016. Equality of opportunity in supervised learning. In Advances in neural information processing systems. 3315--3323."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_47"},{"key":"e_1_3_2_2_37_1","unstructured":"Minyoung Huh Pulkit Agrawal and Alexei A Efros. 2016. What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614 (2016).  Minyoung Huh Pulkit Agrawal and Alexei A Efros. 2016. What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614 (2016)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287600"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/IC4.2009.4909197"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.83"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702520"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298803"},{"key":"e_1_3_2_2_43_1","volume-title":"Proc. 8th Conf. on Innovations in Theoretical Computer Science (ITCS).","author":"Kleinberg Jon","year":"2017"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0981-7"},{"key":"e_1_3_2_2_45_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.  Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105."},{"key":"e_1_3_2_2_46_1","unstructured":"Alina Kuznetsova Hassan Rom Neil Alldrin Jasper Uijlings Ivan Krasin Jordi Pont-Tuset Shahab Kamali Stefan Popov Matteo Malloci Tom Duerig etal 2018. The Open Images dataset v4: Unified image classification object detection and visual relationship detection at scale. arXiv preprint arXiv:1811.00982 (2018).  Alina Kuznetsova Hassan Rom Neil Alldrin Jasper Uijlings Ivan Krasin Jordi Pont-Tuset Shahab Kamali Stefan Popov Matteo Malloci Tom Duerig et al. 2018. The Open Images dataset v4: Unified image classification object detection and visual relationship detection at scale. arXiv preprint arXiv:1811.00982 (2018)."},{"key":"e_1_3_2_2_47_1","unstructured":"Sam Levin. 2016. A beauty contest was judged by AI and the robots didn\u00e2\u0102Źt like dark skin.  Sam Levin. 2016. A beauty contest was judged by AI and the robots didn\u00e2\u0102Źt like dark skin."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299152"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Ziwei Liu Ping Luo Xiaogang Wang and Xiaoou Tang. 2015. Deep Learning Face Attributes in the Wild. In ICCV.  Ziwei Liu Ping Luo Xiaogang Wang and Xiaoou Tang. 2015. Deep Learning Face Attributes in the Wild. In ICCV.","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"e_1_3_2_2_52_1","unstructured":"David Madras Elliot Creager Toniann Pitassi and Richard Zemel. 2018. Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309 (2018).  David Madras Elliot Creager Toniann Pitassi and Richard Zemel. 2018. Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309 (2018)."},{"key":"e_1_3_2_2_53_1","unstructured":"George A Miller. 1998. WordNet: An electronic lexical database. MIT press.  George A Miller. 1998. WordNet: An electronic lexical database. MIT press."},{"key":"e_1_3_2_2_54_1","volume-title":"Model Cards for Model Reporting. In ACM Conference on Fairness, Accountability and Transparency.","author":"Mitchell Margaret","year":"2018"},{"key":"e_1_3_2_2_55_1","unstructured":"Safiya Umoja Noble. 2018. Algorithms of oppression: How search engines reinforce racism. nyu Press.  Safiya Umoja Noble. 2018. Algorithms of oppression: How search engines reinforce racism. nyu Press."},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-5371(65)80064-0"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0025327"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401959"},{"key":"e_1_3_2_2_60_1","unstructured":"Geoff Pleiss Manish Raghavan Felix Wu Jon Kleinberg and Kilian Q Weinberger. 2017. On fairness and calibration. In Advances in Neural Information Processing Systems. 5680--5689.  Geoff Pleiss Manish Raghavan Felix Wu Jon Kleinberg and Kilian Q Weinberger. 2017. On fairness and calibration. In Advances in Neural Information Processing Systems. 5680--5689."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"crossref","unstructured":"Lauren Rhue. 2018. Racial Influence on Automated Perceptions of Emotions. (2018). https:\/\/ssrn.com\/abstract=3281765  Lauren Rhue. 2018. Racial Influence on Automated Perceptions of Emotions. (2018). https:\/\/ssrn.com\/abstract=3281765","DOI":"10.2139\/ssrn.3281765"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"crossref","unstructured":"Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein etal 2015. ImageNet Large Scale Visual Recognition Challenge. International journal of computer vision 115 3 (2015) 211--252.  Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein et al. 2015. ImageNet Large Scale Visual Recognition Challenge. International journal of computer vision 115 3 (2015) 211--252.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_65_1","volume-title":"Proceedings of FATML.","author":"Ryu Hee Jung","year":"2018"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1080\/15205430903359693"},{"key":"e_1_3_2_2_67_1","volume-title":"NeurIPS workshop: Machine Learning for the Developing World.","author":"Shankar Shreya","year":"2017"},{"key":"e_1_3_2_2_68_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Two-stream convolutional networks for action recognition in videos. In Advances in neural information processing systems. 568--576.  Karen Simonyan and Andrew Zisserman. 2014. Two-stream convolutional networks for action recognition in videos. In Advances in neural information processing systems. 568--576."},{"key":"e_1_3_2_2_69_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).  Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_31"},{"key":"e_1_3_2_2_71_1","first-page":"121","article-title":"Measurement of skin color: practical application and theoretical considerations","volume":"44","author":"Hirotsugu Takiwaki","year":"1998","journal-title":"Journal of Medical Investigation"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.128"},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"crossref","unstructured":"Yilun Wang and Michal Kosinski. 2018. Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology (JPSP) (2018).  Yilun Wang and Michal Kosinski. 2018. Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology (JPSP) (2018).","DOI":"10.31234\/osf.io\/hv28a"},{"key":"e_1_3_2_2_76_1","volume-title":"Technical Report CNS-TR-2010-001. California Institute of Technology.","author":"Welinder P.","year":"2010"},{"key":"e_1_3_2_2_77_1","unstructured":"Meredith Whittaker Kate Crawford Roel Dobbe Genevieve Fried Elizabeth Kaziunas Varoon Mathur Sarah Myers West Rashida Richardson Jason Schultz and Oscar Schwartz. 2018. AI Now Report 2018. https:\/\/ainowinstitute.org\/AI_Now_2018_Report.pdf.  Meredith Whittaker Kate Crawford Roel Dobbe Genevieve Fried Elizabeth Kaziunas Varoon Mathur Sarah Myers West Rashida Richardson Jason Schultz and Oscar Schwartz. 2018. AI Now Report 2018. https:\/\/ainowinstitute.org\/AI_Now_2018_Report.pdf."},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174569"},{"key":"e_1_3_2_2_79_1","unstructured":"Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez and Krishna P Gummadi. 2015. Fairness constraints: Mechanisms for fair classification. arXiv preprint arXiv:1507.05259 (2015).  Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez and Krishna P Gummadi. 2015. Fairness constraints: Mechanisms for fair classification. arXiv preprint arXiv:1507.05259 (2015)."},{"key":"e_1_3_2_2_80_1","volume-title":"International Conference on Machine Learning. 325--333","author":"Zemel Rich","year":"2013"},{"key":"e_1_3_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"crossref","unstructured":"Jieyu Zhao Tianlu Wang Mark Yatskar Vicente Ordonez and Kai-Wei Chang. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP.  Jieyu Zhao Tianlu Wang Mark Yatskar Vicente Ordonez and Kai-Wei Chang. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP.","DOI":"10.18653\/v1\/D17-1323"},{"key":"e_1_3_2_2_83_1","volume-title":"Places: A 10 million Image Database for Scene Recognition","author":"Zhou Bolei","year":"2017"}],"event":{"name":"FAT* '20: Conference on Fairness, Accountability, and Transparency","location":"Barcelona Spain","acronym":"FAT* '20","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3375709","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3351095.3375709","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:39Z","timestamp":1750202019000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3375709"}},"subtitle":["filtering and balancing the distribution of the people subtree in the ImageNet hierarchy"],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":81,"alternative-id":["10.1145\/3351095.3375709","10.1145\/3351095"],"URL":"https:\/\/doi.org\/10.1145\/3351095.3375709","relation":{},"subject":[],"published":{"date-parts":[[2020,1,27]]},"assertion":[{"value":"2020-01-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}