{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:30:07Z","timestamp":1792787407967,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":70,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ERC Advanced Grant","award":["741278"],"award-info":[{"award-number":["741278"]}]},{"name":"UKRI Trustworthy Autonomous Systems Node in Verifiability","award":["EP\/V026801\/2"],"award-info":[{"award-number":["EP\/V026801\/2"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,12]]},"DOI":"10.1145\/3597503.3639083","type":"proceedings-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T12:43:26Z","timestamp":1712925806000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Fairness Improvement with Multiple Protected Attributes: How Far Are We?"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4765-1893","authenticated-orcid":false,"given":"Zhenpeng","family":"Chen","sequence":"first","affiliation":[{"name":"University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0481-7264","authenticated-orcid":false,"given":"Jie M.","family":"Zhang","sequence":"additional","affiliation":[{"name":"King's College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-442X","authenticated-orcid":false,"given":"Federica","family":"Sarro","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5864-4488","authenticated-orcid":false,"given":"Mark","family":"Harman","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"1996. The Adult Census Income dataset. https:\/\/archive.ics.uci.edu\/ml\/datasets\/adult."},{"key":"e_1_3_2_1_2_1","unstructured":"2003. U.S. Equal Employment Opportunity Commission. https:\/\/www.eeoc.gov\/initiatives\/e-race\/significant-eeoc-racecolor-casescovering-private-and-federal-sectors#intersectional."},{"key":"e_1_3_2_1_3_1","unstructured":"2015. The Mep15 dataset. https:\/\/meps.ahrq.gov\/mepsweb\/data_stats\/download_data_files_detail.jsp?cboPufNumber=HC-181."},{"key":"e_1_3_2_1_4_1","unstructured":"2016. The Compas dataset. https:\/\/github.com\/propublica\/compas-analysis."},{"key":"e_1_3_2_1_5_1","unstructured":"2016. The Default dataset. https:\/\/archive.ics.uci.edu\/ml\/datasets\/default+of+credit+card+clients."},{"key":"e_1_3_2_1_6_1","unstructured":"2016. Machine bias. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing."},{"key":"e_1_3_2_1_7_1","unstructured":"2016. The Mep16 dataset. https:\/\/meps.ahrq.gov\/mepsweb\/data_stats\/download_data_files_detail.jsp?cboPufNumber=HC-192."},{"key":"e_1_3_2_1_8_1","unstructured":"2018. Study finds gender and skin-type bias in commercial artificial-intelligence systems. https:\/\/news.mit.edu\/2018\/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212."},{"key":"e_1_3_2_1_9_1","unstructured":"2020. Review into bias in algorithmic decision-making. https:\/\/www.gov.uk\/government\/publications\/cdei-publishes-review-into-bias-in-algorithmic-decision-making\/main-report-cdei-review-into-bias-in-algorithmic-decision-making."},{"key":"e_1_3_2_1_10_1","unstructured":"2021. When good algorithms go sexist: Why and how to advance AI gender equity. https:\/\/ssir.org\/articles\/entry\/when_good_algorithms_go_sexist_why_and_how_to_advance_ai_gender_equity."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","unstructured":"2024. Replication package. 10.6084\/m9.figshare.24943590.v2","DOI":"10.6084\/m9.figshare.24943590.v2"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.fsm.4770138"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942287"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ESEM.2017.50"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124118782533"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409704"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468536"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00134"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/184656.180369"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287586"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468537"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409697"},{"key":"e_1_3_2_1_25_1","volume-title":"Fairness testing: A comprehensive survey and analysis of trends. CoRR abs\/2207.10223","author":"Chen Zhenpeng","year":"2022","unstructured":"Zhenpeng Chen, Jie M. Zhang, Max Hort, Federica Sarro, and Mark Harman. 2022. Fairness testing: A comprehensive survey and analysis of trends. CoRR abs\/2207.10223 (2022)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549093"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583561"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_1_29_1","volume-title":"Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, deminist theory and antiracist politics. Feminist Legal Theories","author":"Crenshaw Kimberle","year":"1989","unstructured":"Kimberle Crenshaw. 1989. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, deminist theory and antiracist politics. Feminist Legal Theories (1989), 139--167."},{"key":"e_1_3_2_1_30_1","volume-title":"Retiring adult: New datasets for fair machine learning. Advances in neural information processing systems 34","author":"Ding Frances","year":"2021","unstructured":"Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. 2021. Retiring adult: New datasets for fair machine learning. Advances in neural information processing systems 34 (2021), 6478--6490."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the 16th IEEE International Requirements Engineering Conference, RE 2008. 115--124","author":"Finkelstein Anthony","year":"2008","unstructured":"Anthony Finkelstein, Mark Harman, S. Afshin Mansouri, Jian Ren, and Yuanyuan Zhang. 2008. \"Fairness analysis\" in requirements assignments. In Proceedings of the 16th IEEE International Requirements Engineering Conference, RE 2008. 115--124."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00203"},{"key":"e_1_3_2_1_34_1","first-page":"22","article-title":"Characterizing intersectional group fairness with worst-case comparisons. In Proceedings of the Artificial Intelligence Diversity, Belonging, Equity, and Inclusion","volume":"2021","author":"Ghosh Avijit","year":"2021","unstructured":"Avijit Ghosh, Lea Genuit, and Mary Reagan. 2021. Characterizing intersectional group fairness with worst-case comparisons. In Proceedings of the Artificial Intelligence Diversity, Belonging, Equity, and Inclusion, AIDBEI 2021. 22--34.","journal-title":"AIDBEI"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00133"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems 2016, NIPS 2016. 3315--3323","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. In Proceedings of the Annual Conference on Neural Information Processing Systems 2016, NIPS 2016. 3315--3323."},{"key":"e_1_3_2_1_38_1","volume-title":"Bias mitigation for machine learning classifiers: A comprehensive survey. ACM Journal on Responsible Computing","author":"Hort Max","year":"2023","unstructured":"Max Hort, Zhenpeng Chen, Jie M. Zhang, Mark Harman, and Federica Sarro. 2023. Bias mitigation for machine learning classifiers: A comprehensive survey. ACM Journal on Responsible Computing (2023)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678568"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468565"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.50"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.45"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.09.064"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2019.00115"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-016-9437-5"},{"key":"e_1_3_2_1_48_1","first-page":"25","article-title":"Handling imbalanced datasets: A review","volume":"30","author":"Kotsiantis Sotiris","year":"2006","unstructured":"Sotiris Kotsiantis, Dimitris Kanellopoulos, Panayiotis Pintelas, et al. 2006. Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering 30, 1 (2006), 25--36.","journal-title":"GESTS International Transactions on Computer Science and Engineering"},{"key":"e_1_3_2_1_49_1","volume-title":"Dark-skin individuals are at more risk on the street: Unmasking fairness issues of autonomous driving systems. CoRR abs\/2308.02935","author":"Li Xinyue","year":"2023","unstructured":"Xinyue Li, Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Ying Zhang, and Xuanzhe Liu. 2023. Dark-skin individuals are at more risk on the street: Unmasking fairness issues of autonomous driving systems. CoRR abs\/2308.02935 (2023)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510091"},{"key":"e_1_3_2_1_51_1","volume-title":"On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics","author":"Mann Henry B","year":"1947","unstructured":"Henry B Mann and Donald R Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics (1947), 50--60."},{"key":"e_1_3_2_1_52_1","volume-title":"A survey on bias and fairness in machine learning. ACM Computing Surveys 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 54, 6 (2021), 115:1--115:35."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.70721"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3533767.3534405"},{"key":"e_1_3_2_1_55_1","volume-title":"Research design and statistical analysis","author":"Myers Jerome L","unstructured":"Jerome L Myers, Arnold D Well, and Robert F Lorch Jr. 2013. Research design and statistical analysis. Routledge."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3220713"},{"key":"e_1_3_2_1_57_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems 2017, NIPS 2017. 5680--5689","author":"Pleiss Geoff","unstructured":"Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, and Kilian Q. Weinberger. 2017. On fairness and calibration. In Proceedings of the Annual Conference on Neural Information Processing Systems 2017, NIPS 2017. 5680--5689."},{"key":"e_1_3_2_1_58_1","first-page":"1","article-title":"Imbalanced dataset classification and solutions: A review","volume":"5","author":"Ramyachitra D","year":"2014","unstructured":"D Ramyachitra and Parasuraman Manikandan. 2014. Imbalanced dataset classification and solutions: A review. International Journal of Computing and Business Research 5, 4 (2014), 1--29.","journal-title":"International Journal of Computing and Business Research"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/RE57278.2023.00010"},{"key":"e_1_3_2_1_60_1","volume-title":"Software fairness: An analysis and survey. arXiv preprint arXiv:2205.08809","author":"Soremekun Ezekiel","year":"2022","unstructured":"Ezekiel Soremekun, Mike Papadakis, Maxime Cordy, and Yves Le Traon. 2022. Software fairness: An analysis and survey. arXiv preprint arXiv:2205.08809 (2022)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549169"},{"key":"e_1_3_2_1_62_1","first-page":"101","article-title":"A critique and improvement of the CL common language effect size statistics of McGraw and Wong","volume":"25","author":"Vargha Andr\u00e1s","year":"2000","unstructured":"Andr\u00e1s Vargha and Harold D Delaney. 2000. A critique and improvement of the CL common language effect size statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics 25, 2 (2000), 101--132.","journal-title":"Journal of Educational and Behavioral Statistics"},{"key":"e_1_3_2_1_63_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems 2019","author":"Wick Michael L.","year":"2019","unstructured":"Michael L. Wick, Swetasudha Panda, and Jean-Baptiste Tristan. 2019. Unlocking fairness: A trade-off revisited. In Proceedings of the Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019. 8780--8789."},{"key":"e_1_3_2_1_64_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017. 962--970","author":"Zafar Muhammad Bilal","unstructured":"Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi. 2017. Fairness constraints: Mechanisms for fair classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017. 962--970."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"e_1_3_2_1_66_1","volume-title":"Proceedings of the 43rd IEEE\/ACM International Conference on Software Engineering, ICSE 2021. 1436--1447","author":"Jie","unstructured":"Jie M. Zhang and Mark Harman. 2021. Ignorance and prejudice in software fairness. In Proceedings of the 43rd IEEE\/ACM International Conference on Software Engineering, ICSE 2021. 1436--1447."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2019.2962027"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549103"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380331"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510123"}],"event":{"name":"ICSE '24: IEEE\/ACM 46th International Conference on Software Engineering","location":"Lisbon Portugal","acronym":"ICSE '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS","Faculty of Engineering of University of Porto"]},"container-title":["Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639083","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597503.3639083","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:49:11Z","timestamp":1750272551000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639083"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":70,"alternative-id":["10.1145\/3597503.3639083","10.1145\/3597503"],"URL":"https:\/\/doi.org\/10.1145\/3597503.3639083","relation":{},"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"2024-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}