{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:35:12Z","timestamp":1773246912348,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":66,"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:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"TAILOR - EU Horizon 2020","award":["95221"],"award-info":[{"award-number":["95221"]}]},{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["2020\/09838-0"],"award-info":[{"award-number":["2020\/09838-0"]}]},{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["2020\/10572-5"],"award-info":[{"award-number":["2020\/10572-5"]}]},{"name":"National Council for Scientific and Technological Development (CNPq, Brazil)"},{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["2021\/11086-0"],"award-info":[{"award-number":["2021\/11086-0"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3630106.3658905","type":"proceedings-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T09:14:21Z","timestamp":1717578861000},"page":"279-289","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A preprocessing Shapley value-based approach to detect relevant and disparity prone features in machine learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7301-6167","authenticated-orcid":false,"given":"Guilherme Dean","family":"Pelegrina","sequence":"first","affiliation":[{"name":"School of Applied Sciences, University of Campinas, Brazil and Mackenzie Presbyterian University, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2316-7623","authenticated-orcid":false,"given":"Miguel","family":"Couceiro","sequence":"additional","affiliation":[{"name":"CNRS, LORIA, Universit\u00e9 de Lorraine, France and INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0290-0080","authenticated-orcid":false,"given":"Leonardo Tomazeli","family":"Duarte","sequence":"additional","affiliation":[{"name":"School of Applied Sciences, University of Campinas, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103502"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-90403-0_2"},{"key":"e_1_3_2_2_3_1","volume-title":"International Conference on Machine Learning. PMLR, 60\u201369","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\u201369."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533168"},{"key":"e_1_3_2_2_5_1","volume-title":"Reducing Unintended Bias of ML Models on Tabular and Textual Data. In 8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021","author":"Alves Guilherme","year":"2021","unstructured":"Guilherme Alves, Maxime Amblard, Fabien Bernier, Miguel Couceiro, and Amedeo Napoli. 2021. Reducing Unintended Bias of ML Models on Tabular and Textual Data. In 8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021, Porto, Portugal, October 6-9, 2021. IEEE, 1\u201310."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejdp.2023.100033"},{"key":"e_1_3_2_2_7_1","volume-title":"36th International Conference on Machine Learning, Vol.\u00a097","author":"Ancona Marco","year":"2019","unstructured":"Marco Ancona, Cengiz \u00d6ztireli, and Markus Gross. 2019. Explaining deep neural networks with a polynomial time algorithm for Shapley values approximation. In 36th International Conference on Machine Learning, Vol.\u00a097. PMLR, 272\u2013281."},{"key":"e_1_3_2_2_8_1","unstructured":"Julia. Angwin Jeff. Larson Surya. Mattu and Lauren. Kirchner. 2016. Machine Bias - ProPublica. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN-W.2018.00063"},{"key":"e_1_3_2_2_10_1","volume-title":"Explainability for fair machine learning. ArXiv ID","author":"Begley Tom","year":"2010","unstructured":"Tom Begley, Tobias Schwedes, Christopher Frye, and Ilya Feige. 2020. Explainability for fair machine learning. ArXiv ID: 2010.07389 (2020). http:\/\/arxiv.org\/abs\/2010.07389"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10925-7_40"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3106619"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.18201\/ijisae.2019355381"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2007.19.7.1939"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2009.05.016"},{"key":"e_1_3_2_2_17_1","first-page":"361","article-title":"Statistical Consistency of Kernel Canonical Correlation Analysis","volume":"8","author":"Fukumizu Kenji","year":"2007","unstructured":"Kenji Fukumizu, Francis\u00a0R. Bach, and Arthur Gretton. 2007. Statistical Consistency of Kernel Canonical Correlation Analysis. Journal of Machine Learning Research 8 (2007), 361\u2013383.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_18_1","unstructured":"Kenji Fukumizu Arthur Gretton Xiaohai Sun and Bernhard Sch\u00f6lkopf. 2007. Kernel measures of conditional dependence. In Advances in Neural Information Processing Systems 20 (NIPS)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.59615\/ijie.1.3.38"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488597000440"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30690-2"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533236"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/fintech1010006"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/11564089_7"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11296"},{"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), 3315\u20133323."},{"key":"e_1_3_2_2_27_1","volume-title":"The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Just Hoang\u00a0Anh","year":"2023","unstructured":"Hoang\u00a0Anh Just, Feiyang Kang, Tianhao Wang, Yi Zeng, Myeongseob Ko, Ming Jin, and Ruoxi Jia. 2023. LAVA: Data Valuation without Pre-Specified Learning Algorithms. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103459"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9230-1"},{"key":"e_1_3_2_2_30_1","volume-title":"The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese. In EACL 2024 LT-EDI WorkShop. St. Julians, Malta. https:\/\/hal.science\/hal-04436147","author":"Kulkarni Ajinkya","year":"2024","unstructured":"Ajinkya Kulkarni, Anna Tokareva, Mohammed\u00a0Rameez Qureshi, and Miguel Couceiro. 2024. The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese. In EACL 2024 LT-EDI WorkShop. St. Julians, Malta. https:\/\/hal.science\/hal-04436147"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1452"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108922"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.446"},{"key":"e_1_3_2_2_34_1","volume-title":"Fair & Responsible AI Workshop CHI2020","author":"Lundberg M.","year":"2020","unstructured":"Scott\u00a0M. Lundberg. 2020. Explaining quantitative measures of fairness. In Fair & Responsible AI Workshop CHI2020."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"e_1_3_2_2_36_1","first-page":"I","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg M.","year":"2017","unstructured":"Scott\u00a0M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). 4765\u20134774.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0304-0"},{"key":"e_1_3_2_2_38_1","volume-title":"A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635","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). http:\/\/arxiv.org\/abs\/1908.09635"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-57321-8_2"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445902"},{"key":"e_1_3_2_2_41_1","first-page":"1","article-title":"Sampling Permutations for Shapley Value Estimation","volume":"23","author":"Mitchell Rory","year":"2022","unstructured":"Rory Mitchell, Joshua Cooper, Eibe Frank, and Geoffrey Holmes. 2022. Sampling Permutations for Shapley Value Estimation. Journal of Machine Learning Research 23 (2022), 1\u201346.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_42_1","unstructured":"T. Murofushi and S. Soneda. 1993. Techniques for reading fuzzy measures (III): interaction index. In 9th fuzzy system symposium (Sapporo Japan). 693\u2013696."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533136"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445903"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-023-00363-9"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3298291"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2023.104014"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2023.3295759"},{"key":"e_1_3_2_2_50_1","volume-title":"Shapley value-based approaches to explain the robustness of classifiers in machine learning. ArXiv ID: 2209.04254","author":"Pelegrina Guilherme\u00a0Dean","year":"2022","unstructured":"Guilherme\u00a0Dean Pelegrina and Sajid Siraj. 2022. Shapley value-based approaches to explain the robustness of classifiers in machine learning. ArXiv ID: 2209.04254 (2022). http:\/\/arxiv.org\/abs\/2209.04254"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/778"},{"key":"e_1_3_2_2_52_1","unstructured":"Samira Samadi Uthaipon Tantipongpipat Jamie Morgenstern Mohit Singh and Santosh Vempala. 2018. The price of fair PCA: One extra dimension. In Advances in Neural Information Processing Systems. 10976\u201310987."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00592-x"},{"key":"e_1_3_2_2_54_1","volume-title":"Learning with kernels: Support vector machines, regularization, optimization, and beyond","author":"Sch\u00f6lkopf Bernhard","unstructured":"Bernhard Sch\u00f6lkopf, Alexander\u00a0J. Smola, and Francis Bach. 2002. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press, Cambridge, MA, USA."},{"key":"e_1_3_2_2_55_1","volume-title":"Annals of mathematics studies","author":"Shapley S.","unstructured":"Lloyd\u00a0S. Shapley. 1953. A value for n-person games. In Annals of mathematics studies: Vol. 28. Contributions to the theory of games, Vol. II, W.\u00a0Kuhn and A.\u00a0W. Tucker (Eds.). Princeton University Press, Princeton, 307\u2013317."},{"key":"e_1_3_2_2_56_1","first-page":"1393","article-title":"Feature selection via dependence maximization","volume":"13","author":"Song Le","year":"2012","unstructured":"Le Song, Alex Smola, Arthur Gretton, Justin Bedo, and Karsten Borgwardt. 2012. Feature selection via dependence maximization. Journal of Machine Learning Research 13 (2012), 1393\u20131434.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273600"},{"key":"e_1_3_2_2_58_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML 2020","author":"Sundararajan Mukund","year":"2020","unstructured":"Mukund Sundararajan and Amir Najmi. 2020. The many shapley values for model explanation. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020). PMLR, 9269\u20139278. arXiv:1908.08474"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1201\/b17320"},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.2478\/CAIT-2019-0001"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3194770.3194776"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.103"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107567"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1574-0005(02)03016-3"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"}],"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\/3630106.3658905","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630106.3658905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:29:36Z","timestamp":1755883776000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3658905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":66,"alternative-id":["10.1145\/3630106.3658905","10.1145\/3630106"],"URL":"https:\/\/doi.org\/10.1145\/3630106.3658905","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"}}]}}