{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T13:42:41Z","timestamp":1774705361365,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,21]]},"DOI":"10.1145\/3531146.3533203","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T14:27:10Z","timestamp":1655735230000},"page":"1467-1478","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Adaptive Sampling Strategies to Construct Equitable Training Datasets"],"prefix":"10.1145","author":[{"given":"William","family":"Cai","sequence":"first","affiliation":[{"name":"Stanford, USA"}]},{"given":"Ro","family":"Encarnacion","sequence":"additional","affiliation":[{"name":"Stanford, USA"}]},{"given":"Bobbie","family":"Chern","sequence":"additional","affiliation":[{"name":"Meta, USA"}]},{"given":"Sam","family":"Corbett-Davies","sequence":"additional","affiliation":[{"name":"Meta, USA"}]},{"given":"Miranda","family":"Bogen","sequence":"additional","affiliation":[{"name":"Meta, USA"}]},{"given":"Stevie","family":"Bergman","sequence":"additional","affiliation":[{"name":"Meta, USA"}]},{"given":"Sharad","family":"Goel","sequence":"additional","affiliation":[{"name":"Harvard, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Jacob Abernethy Pranjal Awasthi Matth\u00e4us Kleindessner Jamie Morgenstern Chris Russell and Jie Zhang. 2021. Active Sampling for Min-Max Fairness. arxiv:2006.06879\u00a0[stat.ML]"},{"key":"e_1_3_2_1_2_1","unstructured":"Artificial\u00a0Intelligence Act. 2021. Proposal for a regulation of the European Parliament and the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. EUR-Lex-52021PC0206 (2021)."},{"key":"e_1_3_2_1_3_1","unstructured":"HLEG AI. 2019. High-level Expert Group on Artificial Intelligence."},{"key":"e_1_3_2_1_4_1","unstructured":"Hadis Anahideh Abolfazl Asudeh and Saravanan Thirumuruganathan. 2020. Fair active learning. arXiv preprint arXiv:2001.01796(2020)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s10038-020-00832-7"},{"key":"e_1_3_2_1_6_1","volume-title":"KDD Workshop of Explainable Artificial Intelligence.","author":"Bakker A","year":"2019","unstructured":"Michiel\u00a0A Bakker, Alejandro Noriega-Campero, Duy\u00a0Patrick Tu, Prasanna Sattigeri, Kush\u00a0R Varshney, and AS Pentland. 2019. On fairness in budget-constrained decision making. In KDD Workshop of Explainable Artificial Intelligence."},{"key":"e_1_3_2_1_7_1","volume-title":"Equal opportunity in online classification with partial feedback. Advances in Neural Information Processing Systems 32","author":"Bechavod Yahav","year":"2019","unstructured":"Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, and Steven\u00a0Z Wu. 2019. Equal opportunity in online classification with partial feedback. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00041"},{"key":"e_1_3_2_1_9_1","unstructured":"Richard Berk Hoda Heidari Shahin Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel and Aaron Roth. 2017. A convex framework for fair regression. arXiv preprint arXiv:1706.02409(2017)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1120"},{"key":"e_1_3_2_1_11_1","unstructured":"Fr\u00e9d\u00e9ric Branchaud-Charron Parmida Atighehchian Pau Rodr\u00edguez Grace Abuhamad and Alexandre Lacoste. 2021. Can Active Learning Preemptively Mitigate Fairness Issues?arXiv preprint arXiv:2104.06879(2021)."},{"key":"e_1_3_2_1_12_1","volume-title":"Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency. PMLR, 77\u201391","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency. PMLR, 77\u201391."},{"key":"e_1_3_2_1_13_1","unstructured":"US\u00a0Census Bureau. 2020. Decennial Census."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375823"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_2_1_16_1","volume-title":"Semantics derived automatically from language corpora contain human-like biases. Science 356, 6334","author":"Caliskan Aylin","year":"2017","unstructured":"Aylin Caliskan, Joanna\u00a0J Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science 356, 6334 (2017), 183\u2013186."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xhgg.2020.100017"},{"key":"e_1_3_2_1_18_1","unstructured":"Alex Chohlas-Wood Madison Coots Emma Brunskill and Sharad Goel. 2021. Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making. arXiv preprint arXiv:2109.08792(2021)."},{"key":"e_1_3_2_1_19_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2","author":"Chouldechova Alexandra","year":"2017","unstructured":"Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2 (2017), 153\u2013163."},{"key":"e_1_3_2_1_20_1","unstructured":"Sam Corbett-Davies and Sharad Goel. 2018. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023(2018)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372851"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287572"},{"key":"e_1_3_2_1_24_1","volume-title":"Polygenic risk scores: a biased prediction?Genome Medicine 10, 1","author":"De\u00a0La\u00a0Vega M","year":"2018","unstructured":"Francisco\u00a0M De\u00a0La\u00a0Vega and Carlos\u00a0D Bustamante. 2018. Polygenic risk scores: a biased prediction?Genome Medicine 10, 1 (2018), 1\u20133."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1525-1497.2006.0512.x"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.17"},{"key":"e_1_3_2_1_28_1","volume-title":"Keith Achorn, Anjali Gopi, David Kanter, Max Lam, Mark Mazumder, and Vijay\u00a0Janapa Reddi.","author":"Galvez Daniel","year":"2021","unstructured":"Daniel Galvez, Greg Diamos, Juan Manuel\u00a0Ciro Torres, Keith Achorn, Anjali Gopi, David Kanter, Max Lam, Mark Mazumder, and Vijay\u00a0Janapa Reddi. 2021. The People\u2019s Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. (2021)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458723"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.7326\/M18-3297"},{"key":"e_1_3_2_1_31_1","first-page":"3315","article-title":"Equality of Opportunity in Supervised Learning","volume":"29","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. Advances in Neural Information Processing Systems 29 (2016), 3315\u20133323.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_32_1","volume-title":"Towards Measuring Fairness in AI: the Casual Conversations Dataset","author":"Hazirbas Caner","year":"2021","unstructured":"Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, and Cristian\u00a0Canton Ferrer. 2021. Towards Measuring Fairness in AI: the Casual Conversations Dataset. IEEE Transactions on Biometrics, Behavior, and Identity Science (2021)."},{"key":"e_1_3_2_1_33_1","volume-title":"Increasing Trust in AI Services through Supplier\u2019s Declarations of Conformity. arXiv preprint arXiv:1808.07261 18","author":"Hind Michael","year":"2018","unstructured":"Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan\u00a0Natesan Ramamurthy, Alexandra Olteanu, and Kush\u00a0R Varshney. 2018. Increasing Trust in AI Services through Supplier\u2019s Declarations of Conformity. arXiv preprint arXiv:1808.07261 18 (2018), 2813\u20132869."},{"key":"e_1_3_2_1_34_1","volume-title":"The Dataset Nutrition Label. Data Protection and Privacy: Data Protection and Democracy (2020) 1","author":"Holland Sarah","year":"2020","unstructured":"Sarah Holland, Ahmed Hosny, and Sarah Newman. 2020. The Dataset Nutrition Label. Data Protection and Privacy: Data Protection and Democracy (2020) 1 (2020)."},{"key":"e_1_3_2_1_35_1","volume-title":"Kemen Austin, and Andrew\u00a0Y Ng","author":"Irvin Jeremy","year":"2020","unstructured":"Jeremy Irvin, Hao Sheng, Neel Ramachandran, Sonja Johnson-Yu, Sharon Zhou, Kyle Story, Rose Rustowicz, Cooper Elsworth, Kemen Austin, and Andrew\u00a0Y Ng. 2020. Forestnet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery. arXiv preprint arXiv:2011.05479(2020)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-012-0584-8"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004842"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-018-0183-z"},{"key":"e_1_3_2_1_40_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 277\u2013287","author":"Kilbertus Niki","year":"2020","unstructured":"Niki Kilbertus, Manuel\u00a0Gomez Rodriguez, Bernhard Sch\u00f6lkopf, Krikamol Muandet, and Isabel Valera. 2020. Fair decisions despite imperfect predictions. In International Conference on Artificial Intelligence and Statistics. PMLR, 277\u2013287."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.4230\/LIPIcs.ITCS.2017.43"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1915768117"},{"key":"e_1_3_2_1_43_1","volume-title":"Counterfactual Fairness. In Proceedings of the 31st International Conference on Neural Information Processing Systems.","author":"Kusner Matt","year":"2017","unstructured":"Matt Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual Fairness. In Proceedings of the 31st International Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.2986407"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2017.03.004"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwr160"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.5555\/1032649.1033511"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.23"},{"key":"e_1_3_2_1_49_1","volume-title":"A scientometric review of genome-wide association studies. Communications biology 2, 1","author":"Mills C","year":"2019","unstructured":"Melinda\u00a0C Mills and Charles Rahal. 2019. A scientometric review of genome-wide association studies. Communications biology 2, 1 (2019), 1\u201311."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445902"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_2_1_52_1","unstructured":"Hamed Nilforoshan Johann Gaebler Ravi Shroff and Sharad Goel. 2022. Causal Conceptions of Fairness and their Consequences. Preprint."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314277"},{"key":"e_1_3_2_1_54_1","volume-title":"Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464","author":"Obermeyer Ziad","year":"2019","unstructured":"Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447\u2013453."},{"key":"e_1_3_2_1_55_1","unstructured":"AJ Piergiovanni and Michael\u00a0S. Ryoo. 2020. AViD Dataset: Anonymized Videos from Diverse Countries. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1038\/538161a"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314244"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2507836"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1163"},{"key":"e_1_3_2_1_60_1","volume-title":"Workshop on Real World Experiment Design and Active Learning. International Conference on Machine Learning.","author":"Sharaf Amr","year":"2020","unstructured":"Amr Sharaf and Hal Daum\u00e9\u00a0III. 2020. Promoting fairness in learned models by learning to active learn under parity constraints. In Workshop on Real World Experiment Design and Active Learning. International Conference on Machine Learning."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2019.02.048"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3194770.3194776"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_1_64_1","unstructured":"Muhammad\u00a0Bilal Zafar Isabel Valera Manuel\u00a0Gomez Rodriguez Krishna\u00a0P Gummadi and Adrian Weller. 2017. From Parity to Preference-based Notions of Fairness in Classification. In Advances in Neural Information Processing Systems."}],"event":{"name":"FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency","location":"Seoul Republic of Korea","acronym":"FAccT '22","sponsor":["ACM Association for Computing Machinery"]},"container-title":["2022 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533203","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3531146.3533203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:30Z","timestamp":1750188690000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":64,"alternative-id":["10.1145\/3531146.3533203","10.1145\/3531146"],"URL":"https:\/\/doi.org\/10.1145\/3531146.3533203","relation":{},"subject":[],"published":{"date-parts":[[2022,6,20]]},"assertion":[{"value":"2022-06-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}