{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T12:59:32Z","timestamp":1777121972503,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":53,"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\/3531146.3533136","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T14:27:10Z","timestamp":1655735230000},"page":"715-725","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Achieving Fairness via Post-Processing in Web-Scale Recommender Systems\u2731"],"prefix":"10.1145","author":[{"given":"Preetam","family":"Nandy","sequence":"first","affiliation":[{"name":"LinkedIn Corporation, USA"}]},{"given":"Cyrus","family":"DiCiccio","sequence":"additional","affiliation":[{"name":"While at LinkedIn Corporation, USA"}]},{"given":"Divya","family":"Venugopalan","sequence":"additional","affiliation":[{"name":"LinkedIn Corporation, USA"}]},{"given":"Heloise","family":"Logan","sequence":"additional","affiliation":[{"name":"LinkedIn Corporation, USA"}]},{"given":"Kinjal","family":"Basu","sequence":"additional","affiliation":[{"name":"LinkedIn Corporation, USA"}]},{"given":"Noureddine","family":"El Karoui","sequence":"additional","affiliation":[{"name":"While at LinkedIn Corporation, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Himan Abdollahpouri Masoud Mansoury Robin Burke and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286(2019)."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, Vol.\u00a080","author":"Agarwal Alekh","year":"2018","unstructured":"Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach. 2018. A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning, Vol.\u00a080. PMLR, 60\u201369."},{"key":"e_1_3_2_1_3_1","volume-title":"Addressing Trust Bias for Unbiased Learning-to-Rank. In The World Wide Web Conference(WWW \u201919)","author":"Agarwal Aman","year":"2019","unstructured":"Aman Agarwal, Xuanhui Wang, Cheng Li, Michael Bendersky, and Marc Najork. 2019. Addressing Trust Bias for Unbiased Learning-to-Rank. In The World Wide Web Conference(WWW \u201919). Association for Computing Machinery, New York, NY, USA, 4\u201314."},{"key":"e_1_3_2_1_4_1","volume-title":"KDD (London, United Kingdom)","author":"Agarwal Deepak","unstructured":"Deepak Agarwal, Kinjal Basu, Souvik Ghosh, Ying Xuan, Yang Yang, and Liang Zhang. 2018. Online Parameter Selection for Web-based Ranking Problems. In KDD (London, United Kingdom). ACM, New York, NY, USA, 23\u201332."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753381814"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1257\/mic.20170364"},{"key":"e_1_3_2_1_7_1","unstructured":"Solon Barocas Moritz Hardt and Arvind Narayanan. 2017. Fairness in machine learning. NIPS Tutorial 1(2017)."},{"key":"e_1_3_2_1_8_1","unstructured":"Yahav Bechavod and Katrina Ligett. 2017. Penalizing Unfairness in Binary Classification. (2017). arXiv:1707.00044."},{"key":"e_1_3_2_1_9_1","volume-title":"Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Bello Kevin","year":"2020","unstructured":"Kevin Bello and Jean Honorio. 2020. Fairness constraints can help exact inference in structured prediction. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","unstructured":"B.M. Bolstad R.A Irizarry M. \u00c5strand and T.P. Speed. 2003. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19 2 (01 2003) 185\u2013193. https:\/\/doi.org\/10.1093\/bioinformatics\/19.2.185","DOI":"10.1093\/bioinformatics"},{"key":"e_1_3_2_1_11_1","volume-title":"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_12_1","volume-title":"Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates","author":"Calmon Flavio","unstructured":"Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan\u00a0Ramamurthy, and Kush\u00a0R Varshney. 2017. Optimized Pre-Processing for Discrimination Prevention. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates, Inc., 3992\u20134001."},{"key":"e_1_3_2_1_13_1","unstructured":"L.\u00a0Elisa Celis Damian Straszak and Nisheeth\u00a0K. Vishnoi. 2017. Ranking with Fairness Constraints. arxiv:1704.06840\u00a0[cs.DS]"},{"key":"e_1_3_2_1_14_1","unstructured":"Sam Corbett-Davies and Sharad Goel. 2018. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. arxiv:1808.00023\u00a0[cs.CY]"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372878"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-soc-071913-043455"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330691"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278722"},{"key":"e_1_3_2_1_23_1","unstructured":"Gabriel Goh Andrew Cotter Maya Gupta and Michael\u00a0P Friedlander. 2016. Satisfying real-world goals with dataset constraints. In Advances in Neural Information Processing Systems. 2415\u20132423."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097)","author":"Gordaliza Paula","year":"2019","unstructured":"Paula Gordaliza, Eustasio\u00a0Del Barrio, Gamboa Fabrice, and Jean-Michel Loubes. 2019. Obtaining Fairness using Optimal Transport Theory. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 2357\u20132365."},{"key":"e_1_3_2_1_25_1","volume-title":"Advances in Neural Information Processing Systems 29, D.\u00a0D. Lee, M.\u00a0Sugiyama, U.\u00a0V. Luxburg, I.\u00a0Guyon, and R.\u00a0Garnett (Eds.). Curran Associates","author":"Hardt Moritz","unstructured":"Moritz Hardt, Eric Price, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In Advances in Neural Information Processing Systems 29, D.\u00a0D. Lee, M.\u00a0Sugiyama, U.\u00a0V. Luxburg, I.\u00a0Guyon, and R.\u00a0Garnett (Eds.). Curran Associates, Inc., 3315\u20133323."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462614"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10869-012-9268-3"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a0108)","author":"Jiang Heinrich","year":"2020","unstructured":"Heinrich Jiang and Ofir Nachum. 2020. Identifying and Correcting Label Bias in Machine Learning. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a0108). PMLR, 702\u2013712."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018699"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOAS1201"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.45"},{"key":"e_1_3_2_1_32_1","volume-title":"Machine Learning and Knowledge Discovery in Databases, Peter\u00a0A","author":"Kamishima Toshihiro","unstructured":"Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012. Fairness-Aware Classifier with Prejudice Remover Regularizer. In Machine Learning and Knowledge Discovery in Databases, Peter\u00a0A. Flach, Tijl De\u00a0Bie, and Nello Cristianini (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 35\u201350."},{"key":"e_1_3_2_1_33_1","volume-title":"Counterfactual Fairness. In Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS\u201917)","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(NIPS\u201917). Curran Associates Inc., Red Hook, NY, USA, 4069\u20134079."},{"key":"e_1_3_2_1_34_1","unstructured":"Subha Maity Debarghya Mukherjee Mikhail Yurochkin and Yuekai Sun. 2020. There is no trade-off: enforcing fairness can improve accuracy. arXiv:2011.03173."},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. 4382\u20134391","author":"Mary J\u00e9r\u00e9mie","year":"2019","unstructured":"J\u00e9r\u00e9mie Mary, Cl\u00e9ment Calauzenes, and Noureddine El\u00a0Karoui. 2019. Fairness-aware learning for continuous attributes and treatments. In International Conference on Machine Learning. 4382\u20134391."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/655"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401102"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380255"},{"key":"e_1_3_2_1_39_1","volume-title":"Cambridge University Press","author":"Pearl J.","unstructured":"J. Pearl. 2000. Causality. Models, Reasoning, and Inference. Cambridge University Press, Cambridge."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-SS057"},{"key":"e_1_3_2_1_41_1","unstructured":"Geoff Pleiss Manish Raghavan Felix Wu Jon Kleinberg and Kilian\u00a0Q Weinberger. 2017. On fairness and calibration. In Advances in Neural Information Processing Systems. 5680\u20135689."},{"key":"e_1_3_2_1_42_1","unstructured":"Kailash\u00a0Karthik Saravanakumar. 2021. The Impossibility Theorem of Machine Fairness \u2013 A Causal Perspective. arXiv:2007.06024."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220088"},{"key":"e_1_3_2_1_44_1","volume-title":"Advances in Neural Information Processing Systems 32. Curran Associates","author":"Singh Ashudeep","unstructured":"Ashudeep Singh and Thorsten Joachims. 2019. Policy Learning for Fairness in Ranking. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 5426\u20135436."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412031"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159732"},{"key":"e_1_3_2_1_47_1","volume-title":"On Convexity and Bounds of Fairness-aware Classification. In The World Wide Web Conference. 3356\u20133362","author":"Wu Yongkai","year":"2019","unstructured":"Yongkai Wu, Lu Zhang, and Xintao Wu. 2019. On Convexity and Bounds of Fairness-aware Classification. In The World Wide Web Conference. 3356\u20133362."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_1_49_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a054)","author":"Zafar Muhammad\u00a0Bilal","year":"2017","unstructured":"Muhammad\u00a0Bilal Zafar, Isabel Valera, Manuel\u00a0Gomez Rogriguez, and Krishna\u00a0P. Gummadi. 2017. Fairness Constraints: Mechanisms for Fair Classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a054), Aarti Singhand Jerry Zhu (Eds.). PMLR, Fort Lauderdale, FL, USA, 962\u2013970."},{"key":"e_1_3_2_1_50_1","unstructured":"Muhammad\u00a0Bilal Zafar Isabel Valera Manuel\u00a0Gomez Rogriguez and Krishna\u00a0P Gummadi. 2017. Fairness constraints: Mechanisms for fair classification. In Artificial Intelligence and Statistics. PMLR 962\u2013970."},{"key":"e_1_3_2_1_51_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a028)","author":"Zemel Rich","year":"2013","unstructured":"Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013. Learning Fair Representations. In Proceedings of the 30th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a028), Sanjoy Dasgupta and David McAllester (Eds.). PMLR, Atlanta, Georgia, USA, 325\u2013333."},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a028)","author":"Zemel Rich","year":"2013","unstructured":"Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013. Learning Fair Representations. In Proceedings of the 30th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a028), Sanjoy Dasgupta and David McAllester (Eds.). PMLR, Atlanta, Georgia, USA, 325\u2013333."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-018-05707-8"}],"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\/3531146.3533136","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3531146.3533136","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:08Z","timestamp":1750186928000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":53,"alternative-id":["10.1145\/3531146.3533136","10.1145\/3531146"],"URL":"https:\/\/doi.org\/10.1145\/3531146.3533136","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"}}]}}