{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T02:49:22Z","timestamp":1768272562914,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2020\/09838-0"],"award-info":[{"award-number":["2020\/09838-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2020\/10572-5"],"award-info":[{"award-number":["2020\/10572-5"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2021\/11086-0"],"award-info":[{"award-number":["2021\/11086-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"TAILOR","award":["952215"],"award-info":[{"award-number":["952215"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["AI Ethics"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s43681-023-00363-9","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T11:02:37Z","timestamp":1699959757000},"page":"439-452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A statistical approach to detect disparity prone features in a group fairness setting"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7301-6167","authenticated-orcid":false,"given":"Guilherme Dean","family":"Pelegrina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2316-7623","authenticated-orcid":false,"given":"Miguel","family":"Couceiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0290-0080","authenticated-orcid":false,"given":"Leonardo Tomazeli","family":"Duarte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"363_CR1","volume-title":"Real-world Machine Learning","author":"H Brink","year":"2016","unstructured":"Brink, H., Richards, J., Fetherolf, M.: Real-world Machine Learning. Simon and Schuster, New York (2016)"},{"issue":"3","key":"363_CR2","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3), 160 (2021)","journal-title":"SN Comput. Sci."},{"key":"363_CR3","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)"},{"key":"363_CR4","unstructured":"Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias-ProPublica (2016). https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 16 Oct 2023"},{"key":"363_CR5","doi-asserted-by":"crossref","unstructured":"Raji, I.D., Buolamwini, J.: Actionable auditing: investigating the impact of publicly naming biased performance results of commercial AI products. In: Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society, Honolulu, HI, USA, pp. 429\u2013435 (2019)","DOI":"10.1145\/3306618.3314244"},{"issue":"3","key":"363_CR6","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s43681-021-00108-6","volume":"2","author":"S Wehrli","year":"2022","unstructured":"Wehrli, S., Hertweck, C., Amirian, M., Gl\u00fcge, S., Stadelmann, T.: Bias, awareness, and ignorance in deep-learning-based face recognition. AI Ethics 2(3), 509\u2013522 (2022). https:\/\/doi.org\/10.1007\/s43681-021-00108-6","journal-title":"AI Ethics"},{"issue":"7843","key":"363_CR7","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1038\/s41586-020-03136-0","volume":"589","author":"D Hangartner","year":"2021","unstructured":"Hangartner, D., Kopp, D., Siegenthaler, M.: Monitoring hiring discrimination through online recruitment platforms. Nature 589(7843), 572\u2013576 (2021)","journal-title":"Nature"},{"key":"363_CR8","doi-asserted-by":"crossref","unstructured":"Davidson, T., Bhattacharya, D., Weber, I.: Racial bias in hate speech and abusive language detection datasets. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 25\u201335. Florence, Italy (2019)","DOI":"10.18653\/v1\/W19-3504"},{"key":"363_CR9","doi-asserted-by":"crossref","unstructured":"Calders, T., Kamiran, F., Pechenizkiy, M.: Building classifiers with independency constraints. In: 2009 IEEE International Conference on Data Mining Workshops, pp. 13\u201318 (2009). IEEE","DOI":"10.1109\/ICDMW.2009.83"},{"key":"363_CR10","unstructured":"Roh, Y., Lee, K., Whang, S.E., Suh, C.: Fairbatch: Batch selection for model fairness. In: 9th International Conference on Learning Representations (ICLR) (2021). https:\/\/openreview.net\/forum?id=YNnpaAKeCfx"},{"key":"363_CR11","doi-asserted-by":"crossref","unstructured":"Pelegrina, G.D., Brotto, R.D.B., Duarte, L.T., Attux, R., Romano, J.M.T.: Analysis of trade-offs in fair principal component analysis based on multi-objective optimization. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE, Padua (2022)","DOI":"10.1109\/IJCNN55064.2022.9892809"},{"key":"363_CR12","doi-asserted-by":"publisher","unstructured":"Pelegrina, G.D., Duarte, L.T.: A novel approach for fair principal component analysis based on eigendecomposition. IEEE Transactions on Artificial Intelligence, pp. 1\u201312 (2023). https:\/\/doi.org\/10.1109\/TAI.2023.3298291","DOI":"10.1109\/TAI.2023.3298291"},{"key":"363_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 335\u2013340. New Orleans, LA, USA (2018)","DOI":"10.1145\/3278721.3278779"},{"key":"363_CR14","unstructured":"Agarwal, A., Beygelzimer, A., Dud\u00edk, M., Langford, J., Wallach, H.: A reductions approach to fair classification. In: International Conference on Machine Learning, pp. 60\u201369 (2018). PMLR"},{"key":"363_CR15","doi-asserted-by":"crossref","unstructured":"Iosifidis, V., Ntoutsi, E.: Adafair: cumulative fairness adaptive boosting. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 781\u2013790. Beijing, China (2019)","DOI":"10.1145\/3357384.3357974"},{"key":"363_CR16","first-page":"3315","volume":"29","author":"M Hardt","year":"2016","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Adv. Neural Inf. Process. Syst. 29, 3315\u20133323 (2016)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"363_CR17","doi-asserted-by":"crossref","unstructured":"Fish, B., Kun, J., Lelkes, \u00c1.D.: A confidence-based approach for balancing fairness and accuracy. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 144\u2013152 (2016). SIAM","DOI":"10.1137\/1.9781611974348.17"},{"key":"363_CR18","doi-asserted-by":"crossref","unstructured":"Bhargava, V., Couceiro, M., Napoli, A.: Limeout: an ensemble approach to improve process fairness. In: ECML PKDD 2020 Workshops-Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): XKDD 2020 Proceedings. Communications in Computer and Information Science, vol. 1323, pp. 475\u2013491. Springer, Ghent (2020)","DOI":"10.1007\/978-3-030-65965-3_32"},{"key":"363_CR19","unstructured":"Zemel, R., Wu, Y., Swersky, K., Pitassi, T., Dwork, C.: Learning fair representations. In: Proceedings of the 30th International Conference on Machine Learning, pp. 325\u2013333. Atlanta, Georgia, USA (2013)"},{"key":"363_CR20","doi-asserted-by":"crossref","unstructured":"Iosifidis, V., Fetahu, B., Ntoutsi, E.: Fae: A fairness-aware ensemble framework. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1375\u20131380 (2019). IEEE","DOI":"10.1109\/BigData47090.2019.9006487"},{"key":"363_CR21","doi-asserted-by":"crossref","unstructured":"Alves, G., Amblard, M., Bernier, F., Couceiro, M., Napoli, A.: Reducing unintended bias of ML models on tabular and textual data. In: 8th IEEE International Conference on Data Science and Advanced Analytics. DSAA 2021, pp. 1\u201310. IEEE, Porto (2021)","DOI":"10.1109\/DSAA53316.2021.9564112"},{"key":"363_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejdp.2023.100033","volume":"11","author":"G Alves","year":"2023","unstructured":"Alves, G., Bernier, F., Couceiro, M., Makhlouf, K., Palamidessi, C., Zhioua, S.: Survey on fairness notions and related tensions. EURO J. Decis. Process. 11, 100033 (2023). https:\/\/doi.org\/10.1016\/j.ejdp.2023.100033","journal-title":"EURO J. Decis. Process."},{"key":"363_CR23","doi-asserted-by":"crossref","unstructured":"Grgi\u0107i-Hla\u010da, N., Zafar, M.B., Gummadi, K.P., Weller, A.: Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, pp. 51\u201360. AAAI Press, New Orleans, Lousiana (2018)","DOI":"10.1609\/aaai.v32i1.11296"},{"key":"363_CR24","unstructured":"Grgic-Hlaca, N., Zafar, M.B., Gummadi, K.P., Weller, A.: The case for process fairness in learning: feature selection for fair decision making. In: Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain (2016)"},{"key":"363_CR25","doi-asserted-by":"crossref","unstructured":"Jacobs, A.Z., Wallach, H.: Measurement and fairness. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 375\u2013385 (2021)","DOI":"10.1145\/3442188.3445901"},{"issue":"1","key":"363_CR26","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s00146-021-01154-8","volume":"37","author":"A Tsamados","year":"2022","unstructured":"Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., Floridi, L.: The ethics of algorithms: key problems and solutions. AI Soc. 37(1), 215\u2013230 (2022)","journal-title":"AI Soc."},{"issue":"4","key":"363_CR27","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s43681-021-00067-y","volume":"1","author":"MSA Lee","year":"2021","unstructured":"Lee, M.S.A., Floridi, L., Singh, J.: Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics. AI Ethics 1(4), 529\u2013544 (2021). https:\/\/doi.org\/10.1007\/s43681-021-00067-y","journal-title":"AI Ethics"},{"key":"363_CR28","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214\u2013226. Cambridge, Massachusetts (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"363_CR29","doi-asserted-by":"crossref","unstructured":"Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. SIGIR 2018, pp. 405\u2013414. ACM, Ann Arbor (2018)","DOI":"10.1145\/3209978.3210063"},{"key":"363_CR30","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)"},{"key":"363_CR31","unstructured":"Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems, vol. 30, pp. 4066\u20134076 (2017)"},{"issue":"3","key":"363_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/widm.1452","volume":"12","author":"T Le Quy","year":"2022","unstructured":"Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., Ntoutsi, E.: A survey on datasets for fairness-aware machine learning. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 12(3), 1\u201359 (2022)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"363_CR33","doi-asserted-by":"crossref","unstructured":"Gretton, A., Bousquet, O., Smola, A.J., Sch\u00f6lkopf, B.: Measuring statistical dependence with hilbert-schmidt norms. In: Algorithmic Learning Theory, 16th International Conference, ALT 2005. Lecture Notes in Computer Science, vol. 3734, pp. 63\u201377. Springer, Singapore (2005)","DOI":"10.1007\/11564089_7"},{"key":"363_CR34","volume":"234","author":"T Wang","year":"2021","unstructured":"Wang, T., Dai, X., Liu, Y.: Learning with Hilbert\u2013Schmidt independence criterion: a review and new perspectives. Knowl. Based Syst. 234, 107567 (2021)","journal-title":"Knowl. Based Syst."},{"key":"363_CR35","doi-asserted-by":"crossref","unstructured":"Song, L., Smola, A., Gretton, A., Borgwardt, K.M., Bedo, J.: Supervised feature felection via dependence estimation. In: Proceedings of the 24th International Conference on Machine Learning (ICML), pp. 823\u2013830. Corvalis, Oregon, USA (2007)","DOI":"10.1145\/1273496.1273600"},{"key":"363_CR36","first-page":"1393","volume":"13","author":"L Song","year":"2012","unstructured":"Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K.: Feature selection via dependence maximization. J. Mach. Learn. Res. 13, 1393\u20131434 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"363_CR37","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1016\/j.patcog.2010.12.015","volume":"44","author":"E Barshan","year":"2011","unstructured":"Barshan, E., Ghodsi, A., Azimifar, Z., Jahromi, M.Z.: Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds. Pattern Recognit. 44, 1357\u20131371 (2011)","journal-title":"Pattern Recognit."},{"key":"363_CR38","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.neucom.2019.11.103","volume":"383","author":"H Wang","year":"2020","unstructured":"Wang, H., Ding, Y., Tang, J., Guo, F.: Identification of membrane protein types via multivariate information fusion with Hilbert\u2013Schmidt independence criterion. Neurocomputing 383, 257\u2013269 (2020)","journal-title":"Neurocomputing"},{"key":"363_CR39","unstructured":"Greenfeld, D., Shalit, U.: Robust learning with the hilbert-schmidt independence criterion. In: Proceedings of the 37th International Conference on Machine Learning, pp. 3759\u20133768. PMLR, (2020)"},{"key":"363_CR40","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Suay, A., Laparra, V., Mateo-Garc\u00ed, G., Mu\u00f1oz-Mar\u00ed, J., G\u00f3mez-Chova, L., Camps-Valls, G.: Fair kernel learning. In: Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, vol. 10534, pp. 339\u2013355. Springer, Skopje (2017)","DOI":"10.1007\/978-3-319-71249-9_21"},{"key":"363_CR41","volume":"132","author":"Z Li","year":"2022","unstructured":"Li, Z., P\u00e9rez-Suay, A., Camps-Valls, G., Sejdinovic, D.: Kernel dependence regularizers and gaussian processes with applications to algorithmic fairness. Pattern Recognit. 132, 108922 (2022)","journal-title":"Pattern Recognit."},{"issue":"1","key":"363_CR42","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s10462-011-9230-1","volume":"42","author":"SB Kotsiantis","year":"2011","unstructured":"Kotsiantis, S.B.: Feature selection for machine learning classification problems: a recent overview. Artif. Intell. Rev. 42(1), 157\u2013176 (2011)","journal-title":"Artif. Intell. Rev."},{"key":"363_CR43","unstructured":"Hall, M.A.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)"},{"key":"363_CR44","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","volume":"24","author":"JR Vergara","year":"2014","unstructured":"Vergara, J.R., Est\u00e9vez, P.A.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24, 175\u2013186 (2014)","journal-title":"Neural Comput. Appl."},{"key":"363_CR45","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf, B., Smola, A.J., Bach, F.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)"},{"key":"363_CR46","unstructured":"Fukumizu, K., Gretton, A., Sun, X., Sch\u00f6lkopf, B.: Kernel measures of conditional dependence. In: Advances in Neural Information Processing Systems 20 (NIPS), vol. 20 (2007)"},{"key":"363_CR47","unstructured":"Wightman, L.F.: LSAC national longitudinal bar passage study. Technical report (1998)"},{"key":"363_CR48","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","volume":"36","author":"IC Yeh","year":"2009","unstructured":"Yeh, I.C., Lien, C.: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst. Appl. 36, 2473\u20132480 (2009)","journal-title":"Expert Syst. Appl."}],"container-title":["AI and Ethics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43681-023-00363-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43681-023-00363-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43681-023-00363-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T06:34:11Z","timestamp":1742798051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43681-023-00363-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,14]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["363"],"URL":"https:\/\/doi.org\/10.1007\/s43681-023-00363-9","relation":{},"ISSN":["2730-5953","2730-5961"],"issn-type":[{"value":"2730-5953","type":"print"},{"value":"2730-5961","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,14]]},"assertion":[{"value":"27 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest concerning this research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}