{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:15:37Z","timestamp":1775283337207,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Google Faculty Research Award"},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0100701"],"award-info":[{"award-number":["2018AAA0100701"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Academy of Artificial Intelligence (BAAI)"},{"name":"Amazon Web Service (AWS) Machine Learning for Research Award"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467251","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:09Z","timestamp":1628748729000},"page":"207-217","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility"],"prefix":"10.1145","author":[{"given":"Sen","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weishen","family":"Pan","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changshui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Cornell University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"ProPublica","volume":"23","author":"Angwin Julia","year":"2016","unstructured":"Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016 . Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks . ProPublica , Vol. 23 (2016). Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica , Vol. 23 (2016)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330745"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314234"},{"key":"e_1_3_2_2_4_1","volume-title":"H Chi","author":"Beutel Alex","year":"2017","unstructured":"Alex Beutel , Jilin Chen , Zhe Zhao , and Ed H Chi . 2017 . Data de cisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017). Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_2_2_7_1","volume-title":"Ranking with fairness constraints. arXiv preprint arXiv:1704.06840","author":"Celis L Elisa","year":"2017","unstructured":"L Elisa Celis , Damian Straszak , and Nisheeth K Vishnoi . 2017. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840 ( 2017 ). L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi. 2017. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840 (2017)."},{"key":"e_1_3_2_2_8_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data","author":"Chouldechova Alexandra","year":"2017","unstructured":"Alexandra Chouldechova . 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data , Vol. 5 , 2 ( 2017 ), 153--163. Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data , Vol. 5, 2 (2017), 153--163."},{"key":"e_1_3_2_2_9_1","unstructured":"Andrew Cotter Heinrich Jiang and Karthik Sridharan. 2019. Two-player games for efficient non-convex constrained optimization. In Algorithmic Learning Theory. PMLR 300--332.  Andrew Cotter Heinrich Jiang and Karthik Sridharan. 2019. Two-player games for efficient non-convex constrained optimization. In Algorithmic Learning Theory. PMLR 300--332."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278729"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/945365.964285"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287589"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330691"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"e_1_3_2_2_16_1","unstructured":"Moritz Hardt Eric Price and Nati Srebro. 2016. Equality of opportunity in supervised learning. In Advances in neural information processing systems. 3315--3323.  Moritz Hardt Eric Price and Nati Srebro. 2016. Equality of opportunity in supervised learning. In Advances in neural information processing systems. 3315--3323."},{"key":"e_1_3_2_2_17_1","volume-title":"Greg Ver Steeg, and Aram Galstyan","author":"Harutyunyan Hrayr","year":"2019","unstructured":"Hrayr Harutyunyan , Hrant Khachatrian , David C Kale , Greg Ver Steeg, and Aram Galstyan . 2019 . Multitask learning and benchmarking with clinical time series data. Scientific data , Vol. 6 , 1 (2019), 1--18. Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan. 2019. Multitask learning and benchmarking with clinical time series data. Scientific data , Vol. 6, 1 (2019), 1--18."},{"key":"e_1_3_2_2_18_1","volume-title":"Leo Anthony Celi, and Roger G Mark","author":"Johnson Alistair EW","year":"2016","unstructured":"Alistair EW Johnson , Tom J Pollard , Lu Shen , H Lehman Li-wei, Mengling Feng , Mohammad Ghassemi , Benjamin Moody , Peter Szolovits , Leo Anthony Celi, and Roger G Mark . 2016 . MIMIC-III, a freely accessible critical care database. Scientific data , Vol. 3 (2016), 160035. Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific data , Vol. 3 (2016), 160035."},{"key":"e_1_3_2_2_19_1","volume-title":"Generalizability of predictive models for intensive care unit patients. Machine Learning for Health (ML4H) Workshop at NeurIPS 2018","author":"Johnson Alistair E. W.","year":"2018","unstructured":"Alistair E. W. Johnson , Tom J. Pollard , and Tristan Naumann . 2018. Generalizability of predictive models for intensive care unit patients. Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 ( 2018 ). arxiv: 1812.02275 http:\/\/arxiv.org\/abs\/1812.02275 Alistair E. W. Johnson, Tom J. Pollard, and Tristan Naumann. 2018. Generalizability of predictive models for intensive care unit patients. Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 (2018). arxiv: 1812.02275 http:\/\/arxiv.org\/abs\/1812.02275"},{"key":"e_1_3_2_2_20_1","volume-title":"International Conference on Machine Learning. 2439--2448","author":"Kallus Nathan","year":"2018","unstructured":"Nathan Kallus and Angela Zhou . 2018 . Residual Unfairness in Fair Machine Learning from Prejudiced Data . In International Conference on Machine Learning. 2439--2448 . Nathan Kallus and Angela Zhou. 2018. Residual Unfairness in Fair Machine Learning from Prejudiced Data. In International Conference on Machine Learning. 2439--2448."},{"key":"e_1_3_2_2_21_1","unstructured":"Nathan Kallus and Angela Zhou. 2019. The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric. In Advances in Neural Information Processing Systems. 3433--3443.  Nathan Kallus and Angela Zhou. 2019. The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric. In Advances in Neural Information Processing Systems. 3433--3443."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.83"},{"key":"e_1_3_2_2_23_1","volume-title":"Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807","author":"Kleinberg Jon","year":"2016","unstructured":"Jon Kleinberg , Sendhil Mullainathan , and Manish Raghavan . 2016. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 ( 2016 ). Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 (2016)."},{"key":"e_1_3_2_2_24_1","first-page":"202","article-title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid","volume":"96","author":"Kohavi Ron","year":"1996","unstructured":"Ron Kohavi . 1996 . Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid .. In Kdd , Vol. 96. 202 -- 207 . Ron Kohavi. 1996. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid.. In Kdd , Vol. 96. 202--207.","journal-title":"Kdd"},{"key":"e_1_3_2_2_25_1","volume-title":"50 years of discovery: medical milestones from the National Heart, Lung, and Blood Institute's Framingham Heart Study","author":"Levy Daniel","unstructured":"Daniel Levy . 1999. 50 years of discovery: medical milestones from the National Heart, Lung, and Blood Institute's Framingham Heart Study . Center for Bio-Medical Communication, Inc. Daniel Levy. 1999. 50 years of discovery: medical milestones from the National Heart, Lung, and Blood Institute's Framingham Heart Study .Center for Bio-Medical Communication, Inc."},{"key":"e_1_3_2_2_27_1","volume-title":"The variational fair autoencoder. arXiv preprint arXiv:1511.00830","author":"Louizos Christos","year":"2015","unstructured":"Christos Louizos , Kevin Swersky , Yujia Li , Max Welling , and Richard Zemel . 2015. The variational fair autoencoder. arXiv preprint arXiv:1511.00830 ( 2015 ). Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. 2015. The variational fair autoencoder. arXiv preprint arXiv:1511.00830 (2015)."},{"key":"e_1_3_2_2_28_1","volume-title":"Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309","author":"Madras David","year":"2018","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_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.3053477"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2008.720"},{"key":"e_1_3_2_2_31_1","unstructured":"Harikrishna Narasimhan and Shivani Agarwal. 2013. On the relationship between binary classification bipartite ranking and binary class probability estimation. In Advances in Neural Information Processing Systems. 2913--2921.  Harikrishna Narasimhan and Shivani Agarwal. 2013. On the relationship between binary classification bipartite ranking and binary class probability estimation. In Advances in Neural Information Processing Systems. 2913--2921."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Harikrishna Narasimhan Andrew Cotter Maya R Gupta and Serena Wang. 2020. Pairwise Fairness for Ranking and Regression.. In AAAI. 5248--5255.  Harikrishna Narasimhan Andrew Cotter Maya R Gupta and Serena Wang. 2020. Pairwise Fairness for Ranking and Regression.. In AAAI. 5248--5255.","DOI":"10.1609\/aaai.v34i04.5970"},{"key":"e_1_3_2_2_33_1","volume-title":"Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi.","author":"Pollard Tom J","year":"2018","unstructured":"Tom J Pollard , Alistair EW Johnson , Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi. 2018 . The eICU Collaborative Research Database , a freely available multi-center database for critical care research. Scientific data , Vol. 5 (2018), 180178. Tom J Pollard, Alistair EW Johnson, Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi. 2018. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific data , Vol. 5 (2018), 180178."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220088"},{"key":"e_1_3_2_2_35_1","unstructured":"Ashudeep Singh and Thorsten Joachims. 2019. Policy learning for fairness in ranking. In Advances in Neural Information Processing Systems. 5427--5437.  Ashudeep Singh and Thorsten Joachims. 2019. Policy learning for fairness in ranking. In Advances in Neural Information Processing Systems. 5427--5437."},{"key":"e_1_3_2_2_36_1","volume-title":"Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking. arXiv preprint arXiv:2002.08159","author":"Vogel Robin","year":"2020","unstructured":"Robin Vogel , Aur\u00e9lien Bellet , and St\u00e9phan Cl\u00e9mencc on. 2020. Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking. arXiv preprint arXiv:2002.08159 ( 2020 ). Robin Vogel, Aur\u00e9lien Bellet, and St\u00e9phan Cl\u00e9mencc on. 2020. Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking. arXiv preprint arXiv:2002.08159 (2020)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1053\/gast.2003.50016"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3085504.3085526"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_2_40_1","volume-title":"Manuel Gomez Rodriguez, and Krishna P Gummadi","author":"Zafar Muhammad Bilal","year":"2015","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_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132938"},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference on Machine Learning . 325--333","author":"Zemel Rich","year":"2013","unstructured":"Rich Zemel , Yu Wu , Kevin Swersky , Toni Pitassi , and Cynthia Dwork . 2013 . Learning fair representations . In International Conference on Machine Learning . 325--333 . Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013. Learning fair representations. In International Conference on Machine Learning . 325--333."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467251","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467251","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:28Z","timestamp":1750191508000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":42,"alternative-id":["10.1145\/3447548.3467251","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467251","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}