{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:42:36Z","timestamp":1782279756313,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3630106.3658908","type":"proceedings-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T09:14:21Z","timestamp":1717578861000},"page":"313-325","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A structured regression approach for evaluating model performance across intersectional subgroups"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7405-603X","authenticated-orcid":false,"given":"Christine","family":"Herlihy","sequence":"first","affiliation":[{"name":"University of Maryland, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3119-8270","authenticated-orcid":false,"given":"Kimberly","family":"Truong","sequence":"additional","affiliation":[{"name":"Oregon State University, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2337-9610","authenticated-orcid":false,"given":"Alexandra","family":"Chouldechova","sequence":"additional","affiliation":[{"name":"Microsoft Research, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8565-0621","authenticated-orcid":false,"given":"Miroslav","family":"Dud\u00edk","sequence":"additional","affiliation":[{"name":"Microsoft Research, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Machine Bias: There\u2019s Software Used Across the Country to Predict Future Criminals. And it\u2019s Biased Against Blacks.","author":"Angwin Julia","year":"2016","unstructured":"Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine Bias: There\u2019s Software Used Across the Country to Predict Future Criminals. And it\u2019s Biased Against Blacks. (2016). https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing."},{"key":"e_1_3_2_1_2_1","volume-title":"9th Annual conference of the special interest group for computing, information and society.","author":"Barocas Solon","year":"2017","unstructured":"Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017. The problem with bias: Allocative versus representational harms in machine learning. In 9th Annual conference of the special interest group for computing, information and society."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462610"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176343009"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the 1st Conference on Fairness, Accountability and Transparency(Proceedings of Machine Learning Research, Vol.\u00a081)","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency(Proceedings of Machine Learning Research, Vol.\u00a081), Sorelle\u00a0A. Friedler and Christo Wilson (Eds.). PMLR, 77\u201391."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19778-9_17"},{"key":"e_1_3_2_1_7_1","unstructured":"Kimberl\u00e9 Crenshaw. 1989. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine feminist theory and antiracist politics. Vol.\u00a01989 Article 8."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Bradley Efron and Carl Morris. [n. d.]. Stein\u2019s Estimation Rule and Its Competitors\u2014An Empirical Bayes Approach. J. Amer. Statist. Assoc. 68 341 ([n. d.]) 117\u2013130.","DOI":"10.1080\/01621459.1973.10481350"},{"key":"e_1_3_2_1_9_1","unstructured":"Sergey Feldman Maya Gupta and Bela Frigyik. 2012. Multi-Task Averaging. In Advances in Neural Information Processing Systems Vol.\u00a025."},{"key":"e_1_3_2_1_10_1","volume-title":"International conference on artificial intelligence and statistics. PMLR, 2325\u20132336","author":"Fogliato Riccardo","year":"2020","unstructured":"Riccardo Fogliato, Alexandra Chouldechova, and Max G\u2019Sell. 2020. Fairness evaluation in presence of biased noisy labels. In International conference on artificial intelligence and statistics. PMLR, 2325\u20132336."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.48"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00203"},{"key":"e_1_3_2_1_13_1","volume-title":"Fairness in AI Systems: From Social Context to Practice using Fairlearn. Tutorial presented at the 20th annual Scientific Computing with Python Conference (Scipy","author":"Gandhi Triveni","year":"2021","unstructured":"Triveni Gandhi, Manojit Nandi, Miroslav Dud\u00edk, Hanna Wallach, Michael Madaio, Hilde Weerts, Adrin Jalali, and Lisa Iba\u00f1ez. 2021. Fairness in AI Systems: From Social Context to Practice using Fairlearn. Tutorial presented at the 20th annual Scientific Computing with Python Conference (Scipy 2021), Virtual Event. https:\/\/github.com\/fairlearn\/talks\/tree\/main\/2021_scipy_tutorial"},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Machine Learning. PMLR","author":"H\u00e9bert-Johnson Ursula","year":"2018","unstructured":"Ursula H\u00e9bert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum. 2018. Multicalibration: Calibration for the (computationally-identifiable) masses. In International Conference on Machine Learning. PMLR, 1939\u20131948."},{"key":"e_1_3_2_1_15_1","volume-title":"Proc. Fourth Berkeley Symposium on Mathematical Statistics and Probability. 361\u2013\u2013379","author":"James W.","unstructured":"W. James and C. Stein. 1961. Estimation with quadratic loss. In Proc. Fourth Berkeley Symposium on Mathematical Statistics and Probability. 361\u2013\u2013379."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12371"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2697057"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","unstructured":"Disi Ji Robert\u00a0L. Logan Padhraic Smyth and Mark Steyvers. 2020. Active Bayesian Assessment for Black-Box Classifiers. https:\/\/doi.org\/10.48550\/ARXIV.2002.06532","DOI":"10.48550\/ARXIV.2002.06532"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","unstructured":"Disi Ji Padhraic Smyth and Mark Steyvers. 2020. Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. https:\/\/doi.org\/10.48550\/ARXIV.2010.09851","DOI":"10.48550\/ARXIV.2010.09851"},{"key":"e_1_3_2_1_20_1","volume-title":"International conference on machine learning. PMLR, 2564\u20132572","author":"Kearns Michael","year":"2018","unstructured":"Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei\u00a0Steven Wu. 2018. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International conference on machine learning. PMLR, 2564\u20132572."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1915768117"},{"key":"e_1_3_2_1_22_1","volume-title":"1333\u20131355","author":"Liu Hanzhong","year":"2020","unstructured":"Hanzhong Liu, Xin Xu, and Jingyi\u00a0Jessica Li. 2020. Statistica Sinica 30, 3 (2020), 1333\u20131355."},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 6th Machine Learning for Healthcare Conference. PMLR, 308\u2013336","author":"Miller C.","year":"2021","unstructured":"Andrew\u00a0C. Miller, Leon\u00a0A. Gatys, Joseph Futoma, and Emily Fox. 2021. Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance. In Proceedings of the 6th Machine Learning for Healthcare Conference. PMLR, 308\u2013336. https:\/\/proceedings.mlr.press\/v149\/miller21a.html"},{"key":"e_1_3_2_1_24_1","first-page":"16796","article-title":"Bounding and approximating intersectional fairness through marginal fairness","volume":"35","author":"Molina Mathieu","year":"2022","unstructured":"Mathieu Molina and Patrick Loiseau. 2022. Bounding and approximating intersectional fairness through marginal fairness. Advances in Neural Information Processing Systems 35 (2022), 16796\u201316807.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aax2342"},{"key":"e_1_3_2_1_26_1","volume-title":"Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations. CoRR abs\/2108.06514","author":"Piratla Vihari","year":"2021","unstructured":"Vihari Piratla, Soumen Chakrabarti, and Sunita Sarawagi. 2021. Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations. CoRR abs\/2108.06514 (2021). arXiv:2108.06514https:\/\/arxiv.org\/abs\/2108.06514"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3600211.3604673"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1525\/9780520313880-018"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/781670"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2460276.2460278"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_2_1_32_1","volume-title":"Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. arXiv preprint arXiv:2310.16524","author":"van Breugel Boris","year":"2023","unstructured":"Boris van Breugel, Nabeel Seedat, Fergus Imrie, and Mihaela van\u00a0der Schaar. 2023. Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. arXiv preprint arXiv:2310.16524 (2023)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1221"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533101"},{"key":"e_1_3_2_1_35_1","first-page":"1","article-title":"Fairlearn: Assessing and Improving Fairness of AI Systems","volume":"24","author":"Weerts Hilde","year":"2023","unstructured":"Hilde Weerts, Miroslav Dud\u00edk, Richard Edgar, Adrin Jalali, Roman Lutz, and Michael Madaio. 2023. Fairlearn: Assessing and Improving Fairness of AI Systems. Journal of Machine Learning Research 24, 257 (2023), 1\u20138. http:\/\/jmlr.org\/papers\/v24\/23-0389.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12026"}],"event":{"name":"FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency","location":"Rio de Janeiro Brazil","acronym":"FAccT '24"},"container-title":["The 2024 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3658908","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630106.3658908","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:30:20Z","timestamp":1755883820000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3658908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":36,"alternative-id":["10.1145\/3630106.3658908","10.1145\/3630106"],"URL":"https:\/\/doi.org\/10.1145\/3630106.3658908","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2024-06-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}