{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:35:25Z","timestamp":1790357725924,"version":"4.1.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100020457","name":"C3.ai Digital Transformation Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100020457","id-type":"DOI","asserted-by":"publisher"}]},{"name":"IBM-Illinois Discovery Accelerator Institute"},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["1939725"],"award-info":[{"award-number":["1939725"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AFOSR","award":["FA9550-24-1-0002"],"award-info":[{"award-number":["FA9550-24-1-0002"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100020895","name":"MIT-IBM Watson AI Lab","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100020895","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671797","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:55:12Z","timestamp":1724547312000},"page":"2014-2025","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["AIM: Attributing, Interpreting, Mitigating Data Unfairness"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1828-2109","authenticated-orcid":false,"given":"Zhining","family":"Liu","sequence":"first","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3253-8890","authenticated-orcid":false,"given":"Ruizhong","family":"Qiu","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5534-3401","authenticated-orcid":false,"given":"Zhichen","family":"Zeng","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3338-6371","authenticated-orcid":false,"given":"Yada","family":"Zhu","sequence":"additional","affiliation":[{"name":"IBM Research, Yorktown Heights, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9049-1330","authenticated-orcid":false,"given":"Hendrik","family":"Hamann","sequence":"additional","affiliation":[{"name":"IBM Research, Yorktown Heights, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4405-3887","authenticated-orcid":false,"given":"Hanghang","family":"Tong","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Adel Tameem","year":"2019","unstructured":"Tameem Adel, Isabel Valera, Zoubin Ghahramani, and Adrian Weller. 2019. Onenetwork adversarial fairness. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 2412--2420."},{"key":"e_1_3_2_2_2_1","volume-title":"International conference on machine learning. PMLR, 60--69","author":"Agarwal Alekh","year":"2018","unstructured":"Alekh Agarwal, Alina Beygelzimer, Miroslav Dud\u00edk, John Langford, and Hanna Wallach. 2018. A reductions approach to fair classification. In International conference on machine learning. PMLR, 60--69."},{"key":"e_1_3_2_2_3_1","unstructured":"Mayank Agarwal. 2023. Individual Fairness and inFairness. https:\/\/github.com\/ IBM\/inFairness."},{"key":"e_1_3_2_2_4_1","unstructured":"Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias. In Ethics of Data and Analytics."},{"key":"e_1_3_2_2_5_1","volume-title":"arXiv preprint arXiv:1906.12005","author":"Baharlouei Sina","year":"2019","unstructured":"Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, and Meisam Razaviyayn. 2019. R\\'enyi Fair Inference. arXiv preprint arXiv:1906.12005 (2019)."},{"key":"e_1_3_2_2_6_1","volume-title":"Fairness and machine learning: Limitations and opportunities","author":"Barocas Solon","unstructured":"Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and machine learning: Limitations and opportunities. MIT Press."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942287"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372864"},{"key":"e_1_3_2_2_9_1","volume-title":"Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSRTR- 2020--32","author":"Bird Sarah","year":"2020","unstructured":"Sarah Bird, Miro Dud\u00edk, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, HannaWallach, and KathleenWalker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSRTR- 2020--32 (2020)."},{"key":"e_1_3_2_2_10_1","volume-title":"Fairness in machine learning: A survey. Comput. Surveys","author":"Caton Simon","year":"2020","unstructured":"Simon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. Comput. Surveys (2020)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3659006"},{"key":"e_1_3_2_2_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--163."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1979.10481038"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1988.10478639"},{"key":"e_1_3_2_2_15_1","volume-title":"Statistical models in S","author":"Cleveland William S","unstructured":"William S Cleveland, Eric Grosse, and William M Shyu. 2017. Local regression models. In Statistical models in S. Routledge, 309--376."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0b013e3181a23e3a"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_2_18_1","volume-title":"Abstracting fairness: Oracles, metrics, and interpretability. arXiv preprint arXiv:2004.01840","author":"Dwork Cynthia","year":"2020","unstructured":"Cynthia Dwork, Christina Ilvento, Guy N Rothblum, and Pragya Sur. 2020. Abstracting fairness: Oracles, metrics, and interpretability. arXiv preprint arXiv:2004.01840 (2020)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0177-4"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462621"},{"key":"e_1_3_2_2_21_1","volume-title":"Classification in the presence of label noise: a survey","author":"Fr\u00e9nay Beno\u00eet","year":"2013","unstructured":"Beno\u00eet Fr\u00e9nay and Michel Verleysen. 2013. Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems 25, 5 (2013), 845--869."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2020.0990"},{"key":"e_1_3_2_2_23_1","volume-title":"A survey on concept drift adaptation. ACM computing surveys (CSUR) 46, 4","author":"Gama Jo\u00e3o","year":"2014","unstructured":"Jo\u00e3o Gama, Indr?e ?liobait?e, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. ACM computing surveys (CSUR) 46, 4 (2014), 1--37."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373464.3373470"},{"key":"e_1_3_2_2_25_1","volume-title":"A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406","author":"Han Bo","year":"2020","unstructured":"Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W Tsang, James T Kwok, and Masashi Sugiyama. 2020. A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406 (2020)."},{"key":"e_1_3_2_2_26_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)."},{"key":"e_1_3_2_2_27_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_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.112"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357974"},{"key":"e_1_3_2_2_30_1","volume-title":"Data preprocessing techniques for classification without discrimination. Knowledge and information systems 33, 1","author":"Kamiran Faisal","year":"2012","unstructured":"Faisal Kamiran and Toon Calders. 2012. Data preprocessing techniques for classification without discrimination. Knowledge and information systems 33, 1 (2012), 1--33."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482030"},{"key":"e_1_3_2_2_32_1","first-page":"857","article-title":"Data-driven discrimination at work","volume":"58","author":"Kim Pauline T","year":"2016","unstructured":"Pauline T Kim. 2016. Data-driven discrimination at work. Wm. & Mary L. Rev. 58 (2016), 857.","journal-title":"Wm. & Mary L. Rev."},{"key":"e_1_3_2_2_33_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_2_34_1","unstructured":"Ronny Kohavi and Barry Becker. 1996. Adult Data Set."},{"key":"e_1_3_2_2_35_1","volume-title":"Counterfactual fairness. Advances in neural information processing systems 30","author":"Kusner Matt J","year":"2017","unstructured":"Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual fairness. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_36_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Li Peizhao","year":"2023","unstructured":"Peizhao Li, Ethan Xia, and Hongfu Liu. 2023. Learning antidote data to individual unfairness. In International Conference on Machine Learning. PMLR, 20168--20181."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00078"},{"key":"e_1_3_2_2_38_1","volume-title":"IMBENS: Ensemble class-imbalanced learning in Python. arXiv preprint arXiv:2111.12776","author":"Liu Zhining","year":"2021","unstructured":"Zhining Liu, Jian Kang, Hanghang Tong, and Yi Chang. 2021. IMBENS: Ensemble class-imbalanced learning in Python. arXiv preprint arXiv:2111.12776 (2021)."},{"key":"e_1_3_2_2_39_1","volume-title":"MESA: boost ensemble imbalanced learning with meta-sampler. Advances in neural information processing systems 33","author":"Liu Zhining","year":"2020","unstructured":"Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, and Yi Chang. 2020. MESA: boost ensemble imbalanced learning with meta-sampler. Advances in neural information processing systems 33 (2020), 14463--14474."},{"key":"e_1_3_2_2_40_1","volume-title":"Topological Augmentation for Class-Imbalanced Node Classification. arXiv preprint arXiv:2308.14181","author":"Liu Zhining","year":"2023","unstructured":"Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, and Hanghang Tong. 2023. Topological Augmentation for Class-Imbalanced Node Classification. arXiv preprint arXiv:2308.14181 (2023)."},{"key":"e_1_3_2_2_41_1","volume-title":"A survey on bias and fairness in machine learning. ACM computing surveys (CSUR) 54, 6","author":"Mehrabi Ninareh","year":"2021","unstructured":"Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR) 54, 6 (2021), 1--35."},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference on Learning Representations.","author":"Youssef","unstructured":"Youssef Mroueh et al. 2021. Fair mixup: Fairness via interpolation. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_43_1","volume-title":"International Conference on Machine Learning. PMLR, 7097--7107","author":"Mukherjee Debarghya","year":"2020","unstructured":"Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, and Yuekai Sun. 2020. Two simple ways to learn individual fairness metrics from data. In International Conference on Machine Learning. PMLR, 7097--7107."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014135"},{"key":"e_1_3_2_2_45_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_46_1","volume-title":"Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26691"},{"key":"e_1_3_2_2_48_1","volume-title":"Data mining for discrimination discovery. ACM Transactions on Knowledge Discovery from Data (TKDD) 4, 2","author":"Ruggieri Salvatore","year":"2010","unstructured":"Salvatore Ruggieri, Dino Pedreschi, and Franco Turini. 2010. Data mining for discrimination discovery. ACM Transactions on Knowledge Discovery from Data (TKDD) 4, 2 (2010), 1--40."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807298"},{"key":"e_1_3_2_2_50_1","unstructured":"Richard Sander. 2009. Law School Admissions Dataset. Project SEAPHE. http:\/\/www.seaphe.org\/databases.php."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287598"},{"key":"e_1_3_2_2_52_1","first-page":"621","article-title":"Adjustment of an inverse matrix corresponding to changes in the elements of a given column or a given row of the original matrix","volume":"20","author":"Sherman Jack","year":"1949","unstructured":"Jack Sherman. 1949. Adjustment of an inverse matrix corresponding to changes in the elements of a given column or a given row of the original matrix. Annals of mathematical statistics 20, 4 (1949), 621.","journal-title":"Annals of mathematical statistics"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220046"},{"key":"e_1_3_2_2_54_1","volume-title":"A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002 2, 8","author":"Suresh Harini","year":"2019","unstructured":"Harini Suresh and John V Guttag. 2019. A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002 2, 8 (2019)."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2006.70"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972788.64"},{"key":"e_1_3_2_2_57_1","volume-title":"Robust optimization for fairness with noisy protected groups. Advances in neural information processing systems 33","author":"Wang Serena","year":"2020","unstructured":"Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Michael Jordan. 2020. Robust optimization for fairness with noisy protected groups. Advances in neural information processing systems 33 (2020), 5190--5203."},{"key":"e_1_3_2_2_58_1","volume-title":"International conference on machine learning. PMLR, 11492--11501","author":"Xu Han","year":"2021","unstructured":"Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang. 2021. To be robust or to be fair: Towards fairness in adversarial training. In International conference on machine learning. PMLR, 11492--11501."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16585"},{"key":"e_1_3_2_2_60_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_2_61_1","volume-title":"SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. In International Conference on Learning Representations.","author":"Yurochkin Mikhail","year":"2021","unstructured":"Mikhail Yurochkin and Yuekai Sun. 2021. SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_62_1","volume-title":"International conference on machine learning. PMLR, 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. PMLR, 325--333."},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29605"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-017-0058-x"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/549"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671797","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671797","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671797","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:04:14Z","timestamp":1750277054000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671797"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":66,"alternative-id":["10.1145\/3637528.3671797","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671797","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}