{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T05:44:42Z","timestamp":1771479882031,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":39,"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\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IS-1633387"],"award-info":[{"award-number":["IS-1633387"]}],"id":[{"id":"10.13039\/100000001","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\/3351095.3372839","type":"proceedings-article","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T13:14:18Z","timestamp":1605705258000},"page":"200-209","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Fairness warnings and fair-MAML"],"prefix":"10.1145","author":[{"given":"Dylan","family":"Slack","sequence":"first","affiliation":[{"name":"University of California, Irvine"}]},{"given":"Sorelle A.","family":"Friedler","sequence":"additional","affiliation":[{"name":"Haverford College"}]},{"given":"Emile","family":"Givental","sequence":"additional","affiliation":[{"name":"Haverford College"}]}],"member":"320","published-online":{"date-parts":[[2020,1,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2011. Acquisition preservation and exchange of identification records and information; appointment of officials. U.S. Code §534 (2011).  2011. Acquisition preservation and exchange of identification records and information; appointment of officials. U.S. Code §534 (2011)."},{"key":"e_1_3_2_1_2_1","unstructured":"2013. Developing a National Model for Pretrial Risk Assessment. LJAF Research Summary (2013).  2013. Developing a National Model for Pretrial Risk Assessment. LJAF Research Summary (2013)."},{"key":"e_1_3_2_1_3_1","unstructured":"Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias. ProPublica (2016).  Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias. ProPublica (2016)."},{"key":"e_1_3_2_1_4_1","unstructured":"Solon Barocas Moritz Hardt and Arvind Narayanan. 2018. Fairness and Machine Learning. fairmlbook.org.  Solon Barocas Moritz Hardt and Arvind Narayanan. 2018. Fairness and Machine Learning. fairmlbook.org."},{"key":"e_1_3_2_1_5_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_1_6_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 (2017).  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 (2017)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1755858"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5 2 (2017) 153--163.  Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5 2 (2017) 153--163.","DOI":"10.1089\/big.2016.0047"},{"key":"e_1_3_2_1_10_1","unstructured":"Alexandra Chouldechova and Aaron Roth. 2018. The Frontiers of Fairness in Machine Learning. ArXiv (2018).  Alexandra Chouldechova and Aaron Roth. 2018. The Frontiers of Fairness in Machine Learning. ArXiv (2018)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Ang\u00e8le Christin. 2017. Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society 4 2 (2017).  Ang\u00e8le Christin. 2017. Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society 4 2 (2017).","DOI":"10.1177\/2053951717718855"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314236"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_14_1","volume-title":"Conference on Fairness, Accountability and Transparency. 119--133","author":"Dwork Cynthia","year":"2018"},{"key":"e_1_3_2_1_15_1","unstructured":"The U.S. EEOC. 1979. Uniform guidelines on employee selection procedures. (1979).  The U.S. EEOC. 1979. Uniform guidelines on employee selection procedures. (1979)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.)","volume":"70","author":"Finn Chelsea","year":"2017"},{"key":"e_1_3_2_1_18_1","volume-title":"ACM Conference on Fairness, Accountability and Transparency (FAT*). ACM.","year":"2019"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157469"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Huang Lingxiao","year":"2019"},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.)","volume":"80","author":"Kallus Nathan","year":"2018"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"e_1_3_2_1_23_1","volume-title":"Discriminatory Transfer. Workshop on Fairness, Accountability, and Transparency in Machine Learning","author":"Lan Chao","year":"2017"},{"key":"e_1_3_2_1_24_1","unstructured":"Moshe Lichman. 2013. UCI machine learning repository. (2013).  Moshe Lichman. 2013. UCI machine learning repository. (2013)."},{"key":"e_1_3_2_1_25_1","unstructured":"Zachary C. Lipton Yu-Xiang Wang and Alexander J. Smola. 2018. Detecting and Correcting for Label Shift with Black Box Predictors. ICML (2018).  Zachary C. Lipton Yu-Xiang Wang and Alexander J. Smola. 2018. Detecting and Correcting for Label Shift with Black Box Predictors. ICML (2018)."},{"key":"e_1_3_2_1_26_1","volume-title":"Learning Adversarially Fair and Transferable Representations. International Conference on Machine Learning","author":"Madras David","year":"2018"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888913000039"},{"key":"e_1_3_2_1_29_1","unstructured":"Candice Schumann Xuezhi Wang Alex Beutel Jilin Chen Hai Qian and Ed H. Chi. 2019. Transfer of Machine Learning Fairness across Domains. arXiv (2019).  Candice Schumann Xuezhi Wang Alex Beutel Jilin Chen Hai Qian and Ed H. Chi. 2019. Transfer of Machine Learning Fairness across Domains. arXiv (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"Assessing the Local Interpretability of Machine Learning Models. Workshop on Human-Centric Machine Learning, NeurIPS","author":"Slack Dylan","year":"2019"},{"key":"e_1_3_2_1_31_1","unstructured":"Megan T. Stevenson. 2017. Assessing Risk Assessment in Action. 103 Minnesota Law Review 303 (2017).  Megan T. Stevenson. 2017. Assessing Risk Assessment in Action. 103 Minnesota Law Review 303 (2017)."},{"key":"e_1_3_2_1_32_1","unstructured":"Adarsh Subbaswamy Peter G. Schulam and Suchi Saria. 2018. Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. In AISTATS.  Adarsh Subbaswamy Peter G. Schulam and Suchi Saria. 2018. Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. In AISTATS."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5528-6"},{"key":"e_1_3_2_1_34_1","first-page":"1","article-title":"Learning Optimized Risk Scores","volume":"20","author":"Ustun Berk","year":"2019","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Joaquin Vanschoren. 2019. Meta-Learning. Springer International Publishing Cham 35--61.  Joaquin Vanschoren. 2019. Meta-Learning. Springer International Publishing Cham 35--61.","DOI":"10.1007\/978-3-030-05318-5_2"},{"key":"e_1_3_2_1_36_1","unstructured":"Oriol Vinyals Charles Blundell Timothy P. Lillicrap Koray Kavukcuoglu and Daan Wierstra. 2016. Matching Networks for One Shot Learning. In NeurIPS.  Oriol Vinyals Charles Blundell Timothy P. Lillicrap Koray Kavukcuoglu and Daan Wierstra. 2016. Matching Networks for One Shot Learning. In NeurIPS."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_1_38_1","unstructured":"Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rogriguez and Krishna P Gummadi. 2017. Fairness Constraints: Mechanisms for Fair Classification. In Artificial Intelligence and Statistics. 962--970.  Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rogriguez and Krishna P Gummadi. 2017. Fairness Constraints: Mechanisms for Fair Classification. In Artificial Intelligence and Statistics. 962--970."},{"key":"e_1_3_2_1_39_1","unstructured":"Indre Zliobaite. 2015. A survey on measuring indirect discrimination in machine learning. arXiv (2015).  Indre Zliobaite. 2015. A survey on measuring indirect discrimination in machine learning. arXiv (2015)."}],"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\/3351095.3372839","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3351095.3372839","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3351095.3372839","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:22Z","timestamp":1750200082000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3372839"}},"subtitle":["learning fairly with minimal data"],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":39,"alternative-id":["10.1145\/3351095.3372839","10.1145\/3351095"],"URL":"https:\/\/doi.org\/10.1145\/3351095.3372839","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"}}]}}