{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T21:49:25Z","timestamp":1771710565320,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"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"}],"funder":[{"name":"National Science Foundation","award":["SES-1915790"],"award-info":[{"award-number":["SES-1915790"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,21]]},"DOI":"10.1145\/3531146.3533211","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T14:27:10Z","timestamp":1655735230000},"page":"1552-1560","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["ABCinML: Anticipatory Bias Correction in Machine Learning Applications"],"prefix":"10.1145","author":[{"given":"Abdulaziz A.","family":"Almuzaini","sequence":"first","affiliation":[{"name":"Rutgers University, USA"}]},{"given":"Chidansh A.","family":"Bhatt","sequence":"additional","affiliation":[{"name":"IBM, USA"}]},{"given":"David M.","family":"Pennock","sequence":"additional","affiliation":[{"name":"Rutgers University, USA"}]},{"given":"Vivek K.","family":"Singh","sequence":"additional","affiliation":[{"name":"Rutgers University, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. KDD Cup 2014 - Predicting Excitement at DonorsChoose.org. https:\/\/www.kaggle.com\/c\/kdd-cup-2014-predicting-excitement-at-donors-choose. Accessed: 2021-07-1."},{"key":"e_1_3_2_1_2_1","unstructured":"Robert Adragna Elliot Creager David Madras and Richard Zemel. 2020. Fairness and robustness in invariant learning: A case study in toxicity classification. arXiv preprint arXiv:2011.06485(2020)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3406461"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3347447.3356751"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422841.3423536"},{"key":"e_1_3_2_1_6_1","volume-title":"ProPublica","author":"Angwin Julia","year":"2016","unstructured":"Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica, May 23, 2016 (2016), 139\u2013159."},{"key":"e_1_3_2_1_7_1","unstructured":"Yahav Bechavod and Katrina Ligett. 2017. Penalizing unfairness in binary classification. arXiv preprint arXiv:1707.00044(2017)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124118782533"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3317593"},{"key":"e_1_3_2_1_10_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_11_1","unstructured":"Simon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053(2020)."},{"key":"e_1_3_2_1_12_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2","author":"Chouldechova Alexandra","year":"2017","unstructured":"Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2 (2017), 153\u2013163."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372878"},{"key":"e_1_3_2_1_14_1","volume-title":"Retiring Adult: New Datasets for Fair Machine Learning. arXiv preprint arXiv:2108.04884(2021).","author":"Ding Frances","year":"2021","unstructured":"Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. 2021. Retiring Adult: New Datasets for Fair Machine Learning. arXiv preprint arXiv:2108.04884(2021)."},{"key":"e_1_3_2_1_15_1","unstructured":"Sorelle\u00a0A Friedler Carlos Scheidegger and Suresh Venkatasubramanian. 2016. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236(2016)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287589"},{"key":"e_1_3_2_1_17_1","volume-title":"Predictably unequal? the effects of machine learning on credit markets. The Effects of Machine Learning on Credit Markets (October 1","author":"Fuster Andreas","year":"2020","unstructured":"Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, and Ansgar Walther. 2020. Predictably unequal? the effects of machine learning on credit markets. The Effects of Machine Learning on Credit Markets (October 1, 2020) (2020)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3383418"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373464.3373470"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Swati Gupta Akhil Jalan Gireeja Ranade Helen Yang and Simon Zhuang. 2020. Too Many Fairness Metrics: Is There a Solution?Available at SSRN 3554829(2020).","DOI":"10.2139\/ssrn.3554829"},{"key":"e_1_3_2_1_21_1","volume-title":"Equality of opportunity in supervised learning. Advances in neural information processing systems 29","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Advances in neural information processing systems 29 (2016), 3315\u20133323."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/lnc3.12432"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287600"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Ben Hutchinson Vinodkumar Prabhakaran Emily Denton Kellie Webster Yu Zhong and Stephen Denuyl. 2020. Social biases in NLP models as barriers for persons with disabilities. arXiv preprint arXiv:2005.00813(2020).","DOI":"10.18653\/v1\/2020.acl-main.487"},{"key":"e_1_3_2_1_25_1","volume-title":"FABBOO-Online Fairness-Aware Learning Under Class Imbalance. In International Conference on Discovery Science. Springer, 159\u2013174","author":"Iosifidis Vasileios","year":"2020","unstructured":"Vasileios Iosifidis and Eirini Ntoutsi. 2020. FABBOO-Online Fairness-Aware Learning Under Class Imbalance. In International Conference on Discovery Science. Springer, 159\u2013174."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-27615-7_20"},{"key":"e_1_3_2_1_27_1","unstructured":"Vasileios Iosifidis Wenbin Zhang and Eirini Ntoutsi. 2021. Online Fairness-Aware Learning with Imbalanced Data Streams. arXiv preprint arXiv:2108.06231(2021)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Svetlana Kiritchenko and Saif\u00a0M Mohammad. 2018. Examining gender and race bias in two hundred sentiment analysis systems. arXiv preprint arXiv:1805.04508(2018).","DOI":"10.18653\/v1\/S18-2005"},{"key":"e_1_3_2_1_30_1","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_31_1","volume-title":"International Conference on Machine Learning. PMLR, 5637\u20135664","author":"Koh Pang\u00a0Wei","year":"2021","unstructured":"Pang\u00a0Wei Koh, Shiori Sagawa, Sang\u00a0Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard\u00a0Lanas Phillips, Irena Gao, Tony Lee, 2021. Wilds: A benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning. PMLR, 5637\u20135664."},{"key":"e_1_3_2_1_32_1","unstructured":"Ron Kohavi 1996. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid.. In Kdd Vol.\u00a096. 202\u2013207."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468507.3468518"},{"key":"e_1_3_2_1_34_1","volume-title":"Sebastian Ruder","author":"Lazaridou Angeliki","year":"2021","unstructured":"Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de\u00a0Masson d\u2019Autume, Sebastian Ruder, Dani Yogatama, 2021. Pitfalls of Static Language Modelling. arXiv preprint arXiv:2102.01951(2021)."},{"key":"e_1_3_2_1_35_1","unstructured":"Karima Makhlouf Sami Zhioua and Catuscia Palamidessi. 2020. On the applicability of ML fairness notions. arXiv preprint arXiv:2006.16745(2020)."},{"key":"e_1_3_2_1_36_1","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_37_1","unstructured":"Tomas Mikolov Kai Chen Gregory\u00a0S. Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In ICLR."},{"key":"e_1_3_2_1_38_1","unstructured":"Claire\u00a0Cain Miller. 2015. Can an algorithm hire better than a human. The New York Times 25(2015)."},{"key":"e_1_3_2_1_39_1","unstructured":"Shira Mitchell Eric Potash Solon Barocas Alexander D\u2019Amour and Kristian Lum. 2018. Prediction-based decisions and fairness: A catalogue of choices assumptions and definitions. arXiv preprint arXiv:1811.07867(2018)."},{"key":"e_1_3_2_1_40_1","volume-title":"Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464","author":"Obermeyer Ziad","year":"2019","unstructured":"Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447\u2013453."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Tai\u00a0Le Quy Arjun Roy Vasileios Iosifidis and Eirini Ntoutsi. 2021. A survey on datasets for fairness-aware machine learning. arXiv preprint arXiv:2110.00530(2021).","DOI":"10.1002\/widm.1452"},{"key":"e_1_3_2_1_42_1","unstructured":"Ashkan Rezaei Anqi Liu Omid Memarrast and Brian Ziebart. 2020. Robust Fairness under Covariate Shift. arXiv preprint arXiv:2010.05166(2020)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.17763\/haer.80.1.j94675w001329270"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445865"},{"key":"e_1_3_2_1_45_1","volume-title":"Coopetitive multi-camera surveillance using model predictive control. Machine Vision and applications 19, 5","author":"Singh K","year":"2008","unstructured":"Vivek\u00a0K Singh, Pradeep\u00a0K Atrey, and Mohan\u00a0S Kankanhalli. 2008. Coopetitive multi-camera surveillance using model predictive control. Machine Vision and applications 19, 5 (2008), 375\u2013393."},{"key":"e_1_3_2_1_46_1","unstructured":"Aparicio Sofia. 2020. Predicting Excitement at DonorsChoose.org. https:\/\/github.com\/SofiaAparicio\/KaggleComp_DonorsChoose."},{"key":"e_1_3_2_1_47_1","volume-title":"When training and test sets are different: characterizing learning transfer. Dataset shift in machine learning 30","author":"Storkey Amos","year":"2009","unstructured":"Amos Storkey. 2009. When training and test sets are different: characterizing learning transfer. Dataset shift in machine learning 30 (2009), 3\u201328."},{"key":"e_1_3_2_1_48_1","unstructured":"Harini Suresh and John\u00a0V Guttag. 2019. A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002(2019)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-018-0575-9"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v35i1.2504"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"}],"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\/3531146.3533211","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3531146.3533211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:30Z","timestamp":1750188690000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":51,"alternative-id":["10.1145\/3531146.3533211","10.1145\/3531146"],"URL":"https:\/\/doi.org\/10.1145\/3531146.3533211","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"}}]}}