{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T02:02:47Z","timestamp":1783648967170,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"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":[[2021,7,21]]},"DOI":"10.1145\/3461702.3462523","type":"proceedings-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T01:21:38Z","timestamp":1627694498000},"page":"66-76","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":59,"title":["Minimax Group Fairness: Algorithms and Experiments"],"prefix":"10.1145","author":[{"given":"Emily","family":"Diana","sequence":"first","affiliation":[{"name":"University of Pennsylvania, Amazon AWS AI, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wesley","family":"Gill","sequence":"additional","affiliation":[{"name":"The University of Pennsylvania, Amazon AWS AI, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Kearns","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Amazon AWS AI, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishnaram","family":"Kenthapadi","sequence":"additional","affiliation":[{"name":"Amazon AWS AI, Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Roth","sequence":"additional","affiliation":[{"name":"University of Pennsylvania &amp; Amazon AWS AI, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning .","author":"Agarwal Alekh","year":"2018","unstructured":"Alekh Agarwal , Alina Beygelzimer , Miroslav Dud\u00edk , John Langford , and Hanna Wallach . 2018 . A Reductions Approach to Fair Classification . In Proceedings of the 35th International Conference on Machine Learning . Alekh Agarwal, Alina Beygelzimer, Miroslav Dud\u00edk, John Langford, and Hanna Wallach. 2018. A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning ."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of Machine Learning Research, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Agarwal Alekh","year":"2019","unstructured":"Alekh Agarwal , Miroslav Dudik , and Zhiwei Steven Wu . 2019 . Fair Regression: Quantitative Definitions and Reduction-Based Algorithms . In Proceedings of Machine Learning Research, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) , Vol. 97 . PMLR, Long Beach, California, USA, 120--129. http:\/\/proceedings.mlr.press\/v97\/agarwal19d.html Alekh Agarwal, Miroslav Dudik, and Zhiwei Steven Wu. 2019. Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. In Proceedings of Machine Learning Research, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, California, USA, 120--129. http:\/\/proceedings.mlr.press\/v97\/agarwal19d.html"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/080723491"},{"key":"e_1_3_2_1_4_1","unstructured":"Stephen D. Bay Dennis F. Kibler Michael J. Pazzani and Padhraic Smyth. 2000. The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation.  Stephen D. Bay Dennis F. Kibler Michael J. Pazzani and Padhraic Smyth. 2000. The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1017939242"},{"key":"e_1_3_2_1_6_1","volume-title":"Prediction, Learning, and Games","author":"Cesa-Bianchi Nicolo","unstructured":"Nicolo Cesa-Bianchi and Gabor Lugosi . 2006. Prediction, Learning, and Games . Cambridge University Press . https:\/\/doi.org\/10.1017\/CBO9780511546921 Nicolo Cesa-Bianchi and Gabor Lugosi. 2006. Prediction, Learning, and Games .Cambridge University Press. https:\/\/doi.org\/10.1017\/CBO9780511546921"},{"key":"e_1_3_2_1_7_1","volume-title":"Garnett (Eds.)","volume":"30","author":"Chen Robert S.","year":"2017","unstructured":"Robert S. Chen , Brendan Lucier , Yaron Singer , and Vasilis Syrgkanis . 2017 . Robust Optimization for Non-Convex Objectives. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R . Garnett (Eds.) , Vol. 30 . Curran Associates, Inc., 4705--4714. https:\/\/proceedings.neurips.cc\/paper\/ 2017\/file\/10c66082c124f8afe3df4886f5e516e0-Paper.pdf Robert S. Chen, Brendan Lucier, Yaron Singer, and Vasilis Syrgkanis. 2017. Robust Optimization for Non-Convex Objectives. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc., 4705--4714. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/10c66082c124f8afe3df4886f5e516e0-Paper.pdf"},{"key":"e_1_3_2_1_8_1","volume-title":"Conference on Fairness, Accountability and Transparency. 134--148","author":"Chouldechova Alexandra","year":"2018","unstructured":"Alexandra Chouldechova , Diana Benavides-Prado , Oleksandr Fialko , and Rhema Vaithianathan . 2018 . A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions . In Conference on Fairness, Accountability and Transparency. 134--148 . Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan. 2018. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency. 134--148."},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the 30th International Conference on Algorithmic Learning Theory (Proceedings of Machine Learning Research","volume":"332","author":"Cotter Andrew","year":"2019","unstructured":"Andrew Cotter , Heinrich Jiang , and Karthik Sridharan . 2019 . Two-Player Games for Efficient Non-Convex Constrained Optimization . In Proceedings of the 30th International Conference on Algorithmic Learning Theory (Proceedings of Machine Learning Research , Vol. 98), Aur\u00e9lien Garivier and Satyen Kale (Eds.). PMLR, Chicago, Illinois, 300-- 332 . http:\/\/proceedings.mlr.press\/v98\/cotter19a.html Andrew Cotter, Heinrich Jiang, and Karthik Sridharan. 2019. Two-Player Games for Efficient Non-Convex Constrained Optimization. In Proceedings of the 30th International Conference on Algorithmic Learning Theory (Proceedings of Machine Learning Research, Vol. 98), Aur\u00e9lien Garivier and Satyen Kale (Eds.). PMLR, Chicago, Illinois, 300--332. http:\/\/proceedings.mlr.press\/v98\/cotter19a.html"},{"key":"e_1_3_2_1_10_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml  Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_1_11_1","volume-title":"Conference on Fairness, Accountability and Transparency. 119--133","author":"Dwork Cynthia","year":"2018","unstructured":"Cynthia Dwork , Nicole Immorlica , Adam Tauman Kalai , and Max Leiserson . 2018 . Decoupled classifiers for group-fair and efficient machine learning . In Conference on Fairness, Accountability and Transparency. 119--133 . Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson. 2018. Decoupled classifiers for group-fair and efficient machine learning. In Conference on Fairness, Accountability and Transparency. 119--133."},{"key":"e_1_3_2_1_12_1","volume-title":"A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing","author":"Yongyun Cho Sathishkumar","year":"2020","unstructured":"Sathishkumar V E and Yongyun Cho . 2020. A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing ( 2020 ), 1--18. https:\/\/doi.org\/10.1080\/22797254.2020.1725789 Sathishkumar V E and Yongyun Cho. 2020. A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing (2020), 1--18. https:\/\/doi.org\/10.1080\/22797254.2020.1725789"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.02.007"},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the Ninth Annual Conference on Computational Learning Theory .","author":"Freund Yoav","unstructured":"Yoav Freund and Robert E. Schapire . 1996. Game Theory, On-line Prediction and Boosting . In Proceedings of the Ninth Annual Conference on Computational Learning Theory . Yoav Freund and Robert E. Schapire. 1996. Game Theory, On-line Prediction and Boosting. In Proceedings of the Ninth Annual Conference on Computational Learning Theory ."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/49.103550"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning","author":"Kearns Michael","year":"2018","unstructured":"Michael Kearns , Seth Neel , Aaron Roth , and Zhiwei Steven Wu . 2018 . Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness . In Proceedings of the 35th International Conference on Machine Learning . Stockholm, Sweden, PMLR 80. Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2018. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. In Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden, PMLR 80."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287592"},{"key":"e_1_3_2_1_19_1","volume-title":"Vazirani","author":"Kearns Michael J.","year":"1994","unstructured":"Michael J. Kearns and Umesh V . Vazirani . 1994 . An Introduction to Computational Learning Theory .MIT Press, Cambridge, MA, USA. Michael J. Kearns and Umesh V. Vazirani. 1994. An Introduction to Computational Learning Theory .MIT Press, Cambridge, MA, USA."},{"key":"e_1_3_2_1_20_1","volume-title":"Chi","author":"Lahoti Preethi","year":"2020","unstructured":"Preethi Lahoti , Alex Beutel , Jilin Chen , Kang Lee , Flavien Prost , Nithum Thain , Xuezhi Wang , and Ed H . Chi . 2020 . Fairness without Demographics through Adversarially Reweighted Learning. In Advances in Neural Information Processing Systems . Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H. Chi. 2020. Fairness without Demographics through Adversarially Reweighted Learning. In Advances in Neural Information Processing Systems ."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Martinez Natalie","year":"2020","unstructured":"Natalie Martinez , Martin Bertran , and Guillermo Sapiro . 2020 . Minimax Pareto Fairness: A Multi Objective Perspective . In Proceedings of the 37th International Conference on Machine Learning . Vienna, Austria, PMLR 119. Natalie Martinez, Martin Bertran, and Guillermo Sapiro. 2020. Minimax Pareto Fairness: A Multi Objective Perspective. In Proceedings of the 37th International Conference on Machine Learning. Vienna, Austria, PMLR 119."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2014.03.001"},{"key":"e_1_3_2_1_23_1","unstructured":"ProPublica. 2020. COMPAS Recidivism Risk Score Data and Analysis. Broward County Clerk's Office Broward County Sherrif's Office Florida Department of Corrections ProPublica. https:\/\/www.propublica.org\/datastore\/dataset\/compas-recidivism-risk-score-data-and-analysis  ProPublica. 2020. COMPAS Recidivism Risk Score Data and Analysis. Broward County Clerk's Office Broward County Sherrif's Office Florida Department of Corrections ProPublica. https:\/\/www.propublica.org\/datastore\/dataset\/compas-recidivism-risk-score-data-and-analysis"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Michael Redmond and Alok Baveja. 2002. A data-driven software tool for enabling cooperative information sharing among police departments. 660 - 678 pages. https:\/\/doi.org\/10.1016\/S0377--2217(01)00264--8  Michael Redmond and Alok Baveja. 2002. A data-driven software tool for enabling cooperative information sharing among police departments. 660 - 678 pages. https:\/\/doi.org\/10.1016\/S0377--2217(01)00264--8","DOI":"10.1016\/S0377-2217(01)00264-8"},{"key":"e_1_3_2_1_25_1","unstructured":"Samira Samadi Uthaipon Tantipongpipat Jamie H Morgenstern Mohit Singh and Santosh Vempala. 2018. The price of fair PCA: One extra dimension. In Advances in Neural Information Processing Systems. 10976--10987.  Samira Samadi Uthaipon Tantipongpipat Jamie H Morgenstern Mohit Singh and Santosh Vempala. 2018. The price of fair PCA: One extra dimension. In Advances in Neural Information Processing Systems. 10976--10987."},{"key":"e_1_3_2_1_26_1","volume-title":"Department of Commerce","author":"Bureau of the Census U. S.","year":"1900","unstructured":"Bureau of the Census U. S. Department of Commerce . 1900 . Census Of Population And Housing 1990 United States : Summary Tape File 1a & 3a (Computer Files) . Bureau of the Census U. S. Department of Commerce. 1900. Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files)."},{"key":"e_1_3_2_1_27_1","volume-title":"Department Of Commerce","author":"Bureau Of The Census Producer U.S.","year":"1992","unstructured":"Bureau Of The Census Producer U.S. Department Of Commerce . 1992 . Bureau Of The Census Producer U.S. Department Of Commerce. 1992."},{"key":"e_1_3_2_1_28_1","volume-title":"Department of Justice","author":"Bureau of Justice Statistics U.S.","year":"1992","unstructured":"Bureau of Justice Statistics U.S. Department of Justice . 1992 . Bureau of Justice Statistics U.S. Department of Justice. 1992."},{"key":"e_1_3_2_1_29_1","volume-title":"Department of Justice","author":"Federal Bureau of Investigation U.S.","year":"1995","unstructured":"Federal Bureau of Investigation U.S. Department of Justice . 1995 . Crime in the United States (Computer File) . Federal Bureau of Investigation U.S. Department of Justice. 1995. Crime in the United States (Computer File)."},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Machine Learning. 6373--6382","author":"Ustun Berk","year":"2019","unstructured":"Berk Ustun , Yang Liu , and David Parkes . 2019 . Fairness without harm: Decoupled classifiers with preference guarantees . In International Conference on Machine Learning. 6373--6382 . Berk Ustun, Yang Liu, and David Parkes. 2019. Fairness without harm: Decoupled classifiers with preference guarantees. In International Conference on Machine Learning. 6373--6382."},{"key":"e_1_3_2_1_31_1","volume-title":"Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications","author":"Vapnik V. N.","year":"1971","unstructured":"V. N. Vapnik and A.Y. Chervonenkis . 1971 . Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications , Vol. 16 (1971). https:\/\/doi.org\/10.1137\/1116025 V. N. Vapnik and A.Y. Chervonenkis. 1971. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, Vol. 16 (1971). https:\/\/doi.org\/10.1137\/1116025"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the Twentieth International Conference on Machine Learning","author":"Zinkevich Martin","year":"2003","unstructured":"Martin Zinkevich . 2003 . Online Convex Programming and Generalized Infinitesimal Gradient Ascent . In Proceedings of the Twentieth International Conference on Machine Learning . Washington, DC. Martin Zinkevich. 2003. Online Convex Programming and Generalized Infinitesimal Gradient Ascent. In Proceedings of the Twentieth International Conference on Machine Learning. Washington, DC."}],"event":{"name":"AIES '21: AAAI\/ACM Conference on AI, Ethics, and Society","location":"Virtual Event USA","acronym":"AIES '21","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","AAAI"]},"container-title":["Proceedings of the 2021 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3461702.3462523","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3461702.3462523","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:06Z","timestamp":1750193346000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3461702.3462523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,21]]},"references-count":32,"alternative-id":["10.1145\/3461702.3462523","10.1145\/3461702"],"URL":"https:\/\/doi.org\/10.1145\/3461702.3462523","relation":{},"subject":[],"published":{"date-parts":[[2021,7,21]]},"assertion":[{"value":"2021-07-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}