{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:52:13Z","timestamp":1782280333292,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1909702, IIS1955851"],"award-info":[{"award-number":["IIS-1909702, IIS1955851"]}],"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":[[2022,2,11]]},"DOI":"10.1145\/3488560.3498493","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T21:42:57Z","timestamp":1644961377000},"page":"1433-1442","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Towards Fair Classifiers Without Sensitive Attributes"],"prefix":"10.1145","author":[{"given":"Tianxiang","family":"Zhao","sequence":"first","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enyan","family":"Dai","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Shu","sequence":"additional","affiliation":[{"name":"Illinois Institute of Technology, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhang","family":"Wang","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Arthur Asuncion and David Newman. 2007. UCI machine learning repository."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/mti2030047"},{"key":"e_1_3_2_2_3_1","volume-title":"H Chi","author":"Beutel Alex","year":"2017","unstructured":"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_4_1","volume-title":"Gender differences in the experience of headache. Social science & medicine","author":"Celentano David D","year":"1990","unstructured":"David D Celentano, Martha S Linet, and Walter F Stewart. 1990. Gender differences in the experience of headache. Social science & medicine , Vol. 30, 12 (1990), 1289--1295."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314236"},{"key":"e_1_3_2_2_6_1","volume-title":"Flexibly fair representation learning by disentanglement. arXiv preprint arXiv:1906.02589","author":"Creager Elliot","year":"2019","unstructured":"Elliot Creager, David Madras, J\u00f6rn-Henrik Jacobsen, Marissa A Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel. 2019. Flexibly fair representation learning by disentanglement. arXiv preprint arXiv:1906.02589 (2019)."},{"key":"e_1_3_2_2_7_1","volume-title":"Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information. WSDM","author":"Dai Enyan","year":"2021","unstructured":"Enyan Dai and Suhang Wang. 2021. Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information. WSDM (2021)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106263"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Moritz Hardt Toniann Pitassi Omer Reingold and Richard Zemel. 2012. Fairness through awareness. In ITCS. 214--226.","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_2_10_1","volume-title":"Censoring representations with an adversary. arXiv preprint arXiv:1511.05897","author":"Edwards Harrison","year":"2015","unstructured":"Harrison Edwards and Amos Storkey. 2015. Censoring representations with an adversary. arXiv preprint arXiv:1511.05897 (2015)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Michael Feldman Sorelle A Friedler John Moeller Carlos Scheidegger and Suresh Venkatasubramanian. 2015. Certifying and removing disparate impact. In SIGKDD. 259--268.","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_2_12_1","volume-title":"The VNR concise encyclopedia of mathematics","author":"Gellert Walter","unstructured":"Walter Gellert, M Hellwich, H K\"astner, and H K\u00fcstner. 2012. The VNR concise encyclopedia of mathematics .Springer Science & Business Media."},{"key":"e_1_3_2_2_13_1","volume-title":"Potential biases in machine learning algorithms using electronic health record data. JAMA internal medicine","author":"Gianfrancesco Milena A","year":"2018","unstructured":"Milena A Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, and Gabriela Schmajuk. 2018. Potential biases in machine learning algorithms using electronic health record data. JAMA internal medicine , Vol. 178, 11 (2018), 1544--1547."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/120896219"},{"key":"e_1_3_2_2_15_1","unstructured":"Moritz Hardt Eric Price and Nati Srebro. 2016. Equality of opportunity in supervised learning. In NeurIPS. 3315--3323."},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Hashimoto Tatsunori","year":"2018","unstructured":"Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning. PMLR, 1929--1938."},{"key":"e_1_3_2_2_17_1","volume-title":"Jeff Larson and Lauren Kirchner","author":"Julia Angwin Surya Mattu","year":"2016","unstructured":"Surya Mattu Julia Angwin, Jeff Larson and Lauren Kirchner. 2016. Machine bias: There's software used across the country to predict future criminals and it's biased against blacks. ProPublica (2016)."},{"key":"e_1_3_2_2_18_1","volume-title":"Classifying without discriminating","author":"Kamiran Faisal","unstructured":"Faisal Kamiran and Toon Calders. 2009. Classifying without discriminating. In ICCC. IEEE, 1--6."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403080"},{"key":"e_1_3_2_2_21_1","volume-title":"H 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. arXiv preprint arXiv:2006.13114 (2020)."},{"key":"e_1_3_2_2_22_1","volume-title":"Operationalizing individual fairness with pairwise fair representations. arXiv preprint arXiv:1907.01439","author":"Lahoti Preethi","year":"2019","unstructured":"Preethi Lahoti, Krishna P Gummadi, and Gerhard Weikum. 2019. Operationalizing individual fairness with pairwise fair representations. arXiv preprint arXiv:1907.01439 (2019)."},{"key":"e_1_3_2_2_23_1","unstructured":"Francesco Locatello Gabriele Abbati Thomas Rainforth Stefan Bauer Bernhard Sch\u00f6lkopf and Olivier Bachem. 2019. On the fairness of disentangled representations. In NeurIPS. 14584--14597."},{"key":"e_1_3_2_2_24_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)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Olvi L Mangasarian. 1994. Nonlinear programming .SIAM.","DOI":"10.1137\/1.9781611971255"},{"key":"e_1_3_2_2_26_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_2_27_1","unstructured":"Geoff Pleiss Manish Raghavan Felix Wu Jon Kleinberg and Kilian Q Weinberger. 2017. On fairness and calibration. In NeurIPS. 5680--5689."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2945519"},{"key":"e_1_3_2_2_29_1","volume-title":"Fairness definitions explained. In 2018 ieee\/acm international workshop on software fairness (fairware)","author":"Verma Sahil","unstructured":"Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In 2018 ieee\/acm international workshop on software fairness (fairware). IEEE, 1--7."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10940-015-9265-6"},{"key":"e_1_3_2_2_31_1","unstructured":"Linda F Wightman. 1998. LSAC National Longitudinal Bar Passage Study. LSAC Research Report Series. (1998)."},{"key":"e_1_3_2_2_32_1","volume-title":"Fairgan: Fairness-aware generative adversarial networks. In Big Data","author":"Xu Depeng","year":"2018","unstructured":"Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu. 2018. Fairgan: Fairness-aware generative adversarial networks. In Big Data. IEEE, 570--575."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411980"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.24251\/HICSS.2018.668"},{"key":"e_1_3_2_2_35_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)."},{"key":"e_1_3_2_2_36_1","unstructured":"Rich Zemel Yu Wu Kevin Swersky Toni Pitassi and Cynthia Dwork. 2013. Learning fair representations. In ICML. 325--333."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Brian Hu Zhang Blake Lemoine and Margaret Mitchell. 2018. Mitigating unwanted biases with adversarial learning. In AIES. 335--340.","DOI":"10.1145\/3278721.3278779"},{"key":"e_1_3_2_2_38_1","unstructured":"Chongjie Zhang and Julie A Shah. 2014. Fairness in multi-agent sequential decision-making. (2014)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Lu Zhang Yongkai Wu and Xintao Wu. 2017. Achieving non-discrimination in data release. In SIGKDD. 1335--1344.","DOI":"10.1145\/3097983.3098167"}],"event":{"name":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event AZ USA","acronym":"WSDM '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498493","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3488560.3498493","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498493","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498493","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:19Z","timestamp":1750188679000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498493"}},"subtitle":["Exploring Biases in Related Features"],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":39,"alternative-id":["10.1145\/3488560.3498493","10.1145\/3488560"],"URL":"https:\/\/doi.org\/10.1145\/3488560.3498493","relation":{},"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"2022-02-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}