{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:45:39Z","timestamp":1777675539180,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Swiss National Science Foundation","award":["51NF40 180545"],"award-info":[{"award-number":["51NF40 180545"]}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-20-1-0397"],"award-info":[{"award-number":["FA9550-20-1-0397"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF","award":["915967, 1820942, 1838676"],"award-info":[{"award-number":["915967, 1820942, 1838676"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,3]]},"DOI":"10.1145\/3442188.3445927","type":"proceedings-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T01:45:48Z","timestamp":1614217548000},"page":"648-665","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["A Statistical Test for Probabilistic Fairness"],"prefix":"10.1145","author":[{"given":"Bahar","family":"Taskesen","sequence":"first","affiliation":[{"name":"Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose","family":"Blanchet","sequence":"additional","affiliation":[{"name":"Stanford University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Kuhn","sequence":"additional","affiliation":[{"name":"Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viet Anh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Stanford University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Structured optimal transport. arXiv preprint arXiv:1712.06199","author":"Alvarez-Melis David","year":"2017","unstructured":"David Alvarez-Melis, Tommi S Jaakkola, and Stefanie Jegelka. 2017. Structured optimal transport. arXiv preprint arXiv:1712.06199 (2017)."},{"key":"e_1_3_2_1_2_1","first-page":"671","article-title":"Big data's disparate impact","volume":"104","author":"Barocas Solon","year":"2016","unstructured":"Solon Barocas and Andrew D Selbst. 2016. Big data's disparate impact. California Law Review 104 (2016), 671--732.","journal-title":"California Law Review"},{"key":"e_1_3_2_1_3_1","unstructured":"Rachel KE Bellamy Kuntal Dey Michael Hind Samuel C Hoffman Stephanie Houde Kalapriya Kannan Pranay Lohia Jacquelyn Martino Sameep Mehta Aleksandra Mojsilovic et al. 2018. AI Fairness 360: An extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1137\/141000439"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124118782533"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372845"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1017\/jpr.2019.49"},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Artificial Intelligence and Statistics. 880--889","author":"Blondel Mathieu","year":"2018","unstructured":"Mathieu Blondel, Vivien Seguy, and Antoine Rolet. 2018. Smooth and sparse optimal transport. In International Conference on Artificial Intelligence and Statistics. 880--889."},{"key":"e_1_3_2_1_9_1","volume-title":"Conference on Fairness, Accountability and Transparency. 77--91","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. 77--91."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1137\/17M1143459"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1089\/big.2016.0047"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3376898"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_1_15_1","volume-title":"Optimal transport for domain adaptation","author":"Courty Nicolas","year":"2016","unstructured":"Nicolas Courty, R\u00e9mi Flamary, Devis Tuia, and Alain Rakotomamonjy. 2016. Optimal transport for domain adaptation. IEEE transactions on pattern analysis and machine intelligence 39, 9 (2016), 1853--1865."},{"key":"e_1_3_2_1_16_1","volume-title":"Sinkhorn Distances: Lightspeed Computation of Optimal Transport. In Advances in Neural Information Processing Systems. 2292--2300.","author":"Cuturi Marco","year":"2013","unstructured":"Marco Cuturi. 2013. Sinkhorn Distances: Lightspeed Computation of Optimal Transport. In Advances in Neural Information Processing Systems. 2292--2300."},{"key":"e_1_3_2_1_17_1","volume-title":"Amazon scraps secret AI recruiting tool that showed bias against women","author":"Dastin Jeffrey","year":"2018","unstructured":"Jeffrey Dastin. 2018. Amazon scraps secret AI recruiting tool that showed bias against women. San Fransico, CA: Reuters. Retrieved on October 9 (2018), 2018."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2015-0007"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403199"},{"key":"e_1_3_2_1_20_1","volume-title":"Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm. arXiv preprint arXiv:1802.04367","author":"Dvurechensky Pavel","year":"2018","unstructured":"Pavel Dvurechensky, Alexander Gasnikov, and Alexey Kroshnin. 2018. Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm. arXiv preprint arXiv:1802.04367 (2018)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_22_1","volume-title":"Pappas","author":"Fazlyab Mahyar","year":"2019","unstructured":"Mahyar Fazlyab, Manfred Morari, and George J. Pappas. 2019. Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming. arXiv preprint arXiv:1903.01287 (2019)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1137\/130929886"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-018-5717-1"},{"key":"e_1_3_2_1_26_1","volume-title":"Wasserstein distributional robustness and regularization in statistical learning. arXiv preprint arXiv:1712.06050","author":"Gao Rui","year":"2017","unstructured":"Rui Gao, Xi Chen, and Anton J Kleywegt. 2017. Wasserstein distributional robustness and regularization in statistical learning. arXiv preprint arXiv:1712.06050 (2017)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3317950"},{"key":"e_1_3_2_1_28_1","unstructured":"Aude Genevay Marco Cuturi Gabriel Peyr\u00e9 and Francis Bach. 2016. Stochastic optimization for large-scale optimal transport. In Advances in Neural Information Processing Systems. 3440--3448."},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning. 2357--2365","author":"Gordaliza Paula","year":"2019","unstructured":"Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, and Jean-Michel Loubes. 2019. Obtaining Fairness using Optimal Transport Theory. In Proceedings of the 36th International Conference on Machine Learning. 2357--2365."},{"key":"e_1_3_2_1_30_1","volume-title":"NIPS Symposium on Machine Learning and the Law","volume":"1","author":"Grgic-Hlaca Nina","year":"2016","unstructured":"Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P Gummadi, and Adrian Weller. 2016. The case for process fairness in learning: Feature selection for fair decision making. In NIPS Symposium on Machine Learning and the Law, Vol. 1. 2."},{"key":"e_1_3_2_1_31_1","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. 3315--3323."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305536"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471722146"},{"key":"e_1_3_2_1_34_1","volume-title":"Verifying Individual Fairness in Machine Learning Models. In Conference on Uncertainty in Artificial Intelligence. PMLR, 749--758","author":"John Philips George","year":"2020","unstructured":"Philips George John, Deepak Vijaykeerthy, and Diptikalyan Saha. 2020. Verifying Individual Fairness in Machine Learning Models. In Conference on Uncertainty in Artificial Intelligence. PMLR, 749--758."},{"key":"e_1_3_2_1_35_1","volume-title":"Assessing algorithmic fairness with unobserved protected class using data combination. arXiv preprint arXiv:1906.00285","author":"Kallus Nathan","year":"2019","unstructured":"Nathan Kallus, Xiaojie Mao, and Angela Zhou. 2019. Assessing algorithmic fairness with unobserved protected class using data combination. arXiv preprint arXiv:1906.00285 (2019)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1257\/pandp.20181018"},{"key":"e_1_3_2_1_37_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)."},{"key":"e_1_3_2_1_38_1","volume-title":"Matthew Thorpe, Dejan Slepcev, and Gustavo K Rohde.","author":"Kolouri Soheil","year":"2017","unstructured":"Soheil Kolouri, Se Rim Park, Matthew Thorpe, Dejan Slepcev, and Gustavo K Rohde. 2017. Optimal mass transport: Signal processing and machine-learning applications. IEEE signal processing magazine 34, 4 (2017), 43--59."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299121"},{"key":"e_1_3_2_1_40_1","volume-title":"Viet Anh Nguyen, and Soroosh Shafieezadeh-Abadeh.","author":"Kuhn Daniel","year":"2019","unstructured":"Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen, and Soroosh Shafieezadeh-Abadeh. 2019. Wasserstein distributionally robust optimization: Theory and applications in machine learning. In Operations Research & Management Science in the Age of Analytics. INFORMS, 130--166."},{"key":"e_1_3_2_1_41_1","unstructured":"Zachary Lipton Julian McAuley and Alexandra Chouldechova. 2018. Does mitigating ML's impact disparity require treatment disparity?. In Advances in Neural Information Processing Systems. 8125--8135."},{"key":"e_1_3_2_1_42_1","volume-title":"International Conference on Machine Learning.","author":"Lohaus Michael","year":"2020","unstructured":"Michael Lohaus, Micha\u00ebl Perrot, and Ulrike von Luxburg. 2020. Too Relaxed to Be Fair. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsa1507092"},{"key":"e_1_3_2_1_44_1","volume-title":"A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635","author":"Mehrabi Ninareh","year":"2019","unstructured":"Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)."},{"key":"e_1_3_2_1_45_1","unstructured":"Gaspard Monge. 1781. M\u00e9moire sur la th\u00e9orie des d\u00e9blais et des remblais. Histoire de l'Acad\u00e9mie Royale des Sciences de Paris (1781)."},{"key":"e_1_3_2_1_46_1","unstructured":"MultiMedia LLC. 2016 (accessed June 4 2020). Machine Bias. Available at https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00473"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOS1065"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-017-0725-5"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88690-7_37"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459199"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1561\/9781680835519"},{"key":"e_1_3_2_1_54_1","unstructured":"Geoff Pleiss Manish Raghavan Felix Wu Jon Kleinberg and Kilian Q Weinberger. 2017. On fairness and calibration. In Advances in Neural Information Processing Systems. 5680--5689."},{"key":"e_1_3_2_1_55_1","unstructured":"Antoine Rolet Marco Cuturi and Gabriel Peyr\u00e9. 2016. Fast dictionary learning with a smoothed Wasserstein loss. In Artificial Intelligence and Statistics. 630--638."},{"key":"e_1_3_2_1_56_1","volume-title":"Carlo Tomasi, and Leonidas J Guibas","author":"Rubner Yossi","year":"2000","unstructured":"Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas. 2000. The earth mover's distance as a metric for image retrieval. International journal of computer vision 40, 2 (2000), 99--121."},{"key":"e_1_3_2_1_57_1","volume-title":"Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577","author":"Saleiro Pedro","year":"2018","unstructured":"Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T Rodolfa, and Rayid Ghani. 2018. Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577 (2018)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-016-0653-9"},{"key":"e_1_3_2_1_59_1","unstructured":"Vivien Seguy and Marco Cuturi. 2015. Principal geodesic analysis for probability measures under the optimal transport metric. In Advances in Neural Information Processing Systems. 3312--3320."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.43.5.807"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766963"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601175"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1137\/16M1067494"},{"key":"e_1_3_2_1_64_1","volume-title":"Daniel Kuhn, and Jose Blanchet.","author":"Taskesen Bahar","year":"2020","unstructured":"Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, and Jose Blanchet. 2020. A Distributionally Robust Approach to Fair Classification. arXiv preprint arXiv:2007.09530 (2020)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-017-0726-4"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2017.29"},{"key":"e_1_3_2_1_67_1","volume-title":"The what-if tool: Interactive probing of machine learning models","author":"Wexler James","year":"2019","unstructured":"James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Vi\u00e9gas, and Jimbo Wilson. 2019. The what-if tool: Interactive probing of machine learning models. IEEE transactions on visualization and computer graphics 26, 1 (2019), 56--65."},{"key":"e_1_3_2_1_68_1","volume-title":"Auditing ML Models for Individual Bias and Unfairness. arXiv preprint arXiv:2003.05048","author":"Xue Songkai","year":"2020","unstructured":"Songkai Xue, Mikhail Yurochkin, and Yuekai Sun. 2020. Auditing ML Models for Individual Bias and Unfairness. arXiv preprint arXiv:2003.05048 (2020)."},{"key":"e_1_3_2_1_69_1","volume-title":"International Conference on Learning Representations.","author":"Yurochkin Mikhail","year":"2020","unstructured":"Mikhail Yurochkin, Amanda Bower, and Yuekai Sun. 2020. Training individually fair ML models with sensitive subspace robustness. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_1_71_1","volume-title":"Manuel Gomez Rodriguez, and Krishna P Gummadi","author":"Zafar Muhammad Bilal","year":"2017","unstructured":"Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017. Fairness constraints: Mechanisms for fair classification. AISTATS (2017)."},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.orl.2018.01.011"}],"event":{"name":"FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency","location":"Virtual Event Canada","acronym":"FAccT '21","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445927","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442188.3445927","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442188.3445927","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:57Z","timestamp":1750193337000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445927"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":71,"alternative-id":["10.1145\/3442188.3445927","10.1145\/3442188"],"URL":"https:\/\/doi.org\/10.1145\/3442188.3445927","relation":{},"subject":[],"published":{"date-parts":[[2021,3]]},"assertion":[{"value":"2021-03-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}