{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T04:54:37Z","timestamp":1771649677406,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["AI &amp; Soc"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Using biased data to train Artificial Intelligence (AI) algorithms will lead to biased decisions, discriminating against certain groups or individuals. Bias can be explicit (one or several protected features directly influence the decisions) or implicit (one or several protected features indirectly influence the decisions). Unsurprisingly, biased patterns are difficult to detect and mitigate. This paper investigates the extent to which explicit and implicit against one or more protected features in structured classification data sets can be mitigated simultaneously while retaining the data\u2019s discriminatory power. The main contribution of this paper concerns an optimization-based bias mitigation method that reweights the training instances. The algorithm operates with numerical and nominal data and can mitigate implicit and explicit bias against several protected features simultaneously. The trade-off between bias mitigation and accuracy loss can be controlled using parameters in the objective function. The numerical simulations using real-world data sets show a reduction of up to 77% of implicit bias and a complete removal of explicit bias against protected features at no cost of accuracy of a wrapper classifier trained on the data. Overall, the proposed method outperforms the state-of-the-art bias mitigation methods for the selected data sets.<\/jats:p>","DOI":"10.1007\/s00146-024-02003-0","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T04:02:04Z","timestamp":1720584124000},"page":"2551-2570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mitigating implicit and explicit bias in structured data without sacrificing accuracy in pattern classification"],"prefix":"10.1007","volume":"40","author":[{"given":"Fabian","family":"Hoitsma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-3701","authenticated-orcid":false,"given":"Gonzalo","family":"N\u00e1poles","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c7i\u00e7ek","family":"G\u00fcven","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yamisleydi","family":"Salgueiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"issue":"4","key":"2003_CR1","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/TEVC.2003.814633","volume":"7","author":"CW Ahn","year":"2003","unstructured":"Ahn CW, Ramakrishna R (2003) Elitism-based compact genetic algorithms. IEEE Trans Evol Comput 7(4):367\u2013385. https:\/\/doi.org\/10.1109\/TEVC.2003.814633","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"2003_CR2","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/s00778-021-00671-8","volume":"30","author":"A Balayn","year":"2021","unstructured":"Balayn A, Lofi C, Houben G-J (2021) Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems. VLDB J 30(5):739\u2013768. https:\/\/doi.org\/10.1007\/s00778-021-00671-8","journal-title":"VLDB J"},{"key":"2003_CR3","unstructured":"Bellamy RKE, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, Zhang Y (2018) AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv:1810.01943"},{"issue":"1","key":"2003_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41060-016-0040-z","volume":"3","author":"F Bonchi","year":"2017","unstructured":"Bonchi F, Hajian S, Mishra B, Ramazzotti D (2017) Exposing the probabilistic causal structure of discrimination. Int J Data Sci Anal 3(1):1\u201321. https:\/\/doi.org\/10.1007\/s41060-016-0040-z","journal-title":"Int J Data Sci Anal"},{"key":"2003_CR5","doi-asserted-by":"crossref","unstructured":"Calders T, Kamiran F, Pechenizkiy M (2009) Building classifiers with independency constraints. 2009 IEEE international conference on data mining workshops (pp 13\u201318)","DOI":"10.1109\/ICDMW.2009.83"},{"key":"2003_CR6","unstructured":"Calmon F, Wei D, Vinzamuri B, Natesan\u00a0Ramamurthy K, Varshney KR (2017) Optimized pre-processing for discrimination prevention. (Vol.\u00a030). Curran Associates, Inc"},{"key":"2003_CR7","doi-asserted-by":"publisher","DOI":"10.2196\/43251","volume":"25","author":"Y Chen","year":"2023","unstructured":"Chen Y, Clayton EW, Novak LL, Anders S, Malin B (2023) Human-centered design to address biases in artificial intelligence. J Med Internet Res 25:e43251. https:\/\/doi.org\/10.2196\/43251","journal-title":"J Med Internet Res"},{"key":"2003_CR8","doi-asserted-by":"crossref","unstructured":"Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM sigkdd international conference on knowledge discovery and data mining (pp 797\u2013806). Association for Computing Machinery","DOI":"10.1145\/3097983.3098095"},{"key":"2003_CR9","unstructured":"Cortez P, Silva AMG (2008) Using data mining to predict secondary school student performance"},{"key":"2003_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00146-022-01494-z","author":"G Curto","year":"2022","unstructured":"Curto G, Jojoa Acosta MF, Comim F, Garcia-Zapirain B (2022) Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings. AI Soc. https:\/\/doi.org\/10.1007\/s00146-022-01494-z","journal-title":"AI Soc"},{"key":"2003_CR11","unstructured":"Dua D, Graff C (2017) Machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"3","key":"2003_CR12","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1080\/01969727308546046","volume":"3","author":"JC Dunn","year":"1973","unstructured":"Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybern 3(3):32\u201357. https:\/\/doi.org\/10.1080\/01969727308546046","journal-title":"J Cybern"},{"key":"2003_CR13","doi-asserted-by":"crossref","unstructured":"Feldman M, Friedler SA, Moeller J, Scheidegger C, Venkatasubramanian S (2015) Certifying and removing disparate impact. In: Proceedings of the 21th ACM sigkdd international conference on knowledge discovery and data mining (pp 259\u2013268). Association for Computing Machinery","DOI":"10.1145\/2783258.2783311"},{"key":"2003_CR14","unstructured":"Fuchs DJ (2018) The dangers of human-like bias in machine-learning algorithms. Missouri S &T\u2019s Peer to Peer 2(1):1. https:\/\/scholarsmine.mst.edu\/peer2peer\/vol2\/iss1\/1"},{"key":"2003_CR15","doi-asserted-by":"crossref","unstructured":"Grau I, N\u00e1poles G, Hoitsma F, Koumeri LK, Vanhoof K (2024) Measuring implicit bias using shap feature importance and fuzzy cognitive maps. In: Arai K (ed) Intelligent systems and applications (pp 745\u2013764). Springer Nature Switzerland","DOI":"10.1007\/978-3-031-47721-8_50"},{"issue":"7","key":"2003_CR16","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TKDE.2012.72","volume":"25","author":"S Hajian","year":"2012","unstructured":"Hajian S, Domingo-Ferrer J (2012) A methodology for direct and indirect discrimination prevention in data mining. IEEE Trans Knowl Data Eng 25(7):1445\u20131459. https:\/\/doi.org\/10.1109\/TKDE.2012.72","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2003_CR17","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Advances in neural information processing systems (Vol.\u00a029). Curran Associates, Inc"},{"key":"2003_CR18","volume-title":"Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence","author":"JH Holland","year":"1975","unstructured":"Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. U Michigan Press"},{"issue":"1","key":"2003_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran F, Calders T (2012) Data preprocessing techniques for classification without discrimination. Knowl Inf Syst 33(1):1\u201333. https:\/\/doi.org\/10.1007\/s10115-011-0463-8","journal-title":"Knowl Inf Syst"},{"issue":"1","key":"2003_CR20","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/S0020-7373(86)80040-2","volume":"24","author":"B Kosko","year":"1986","unstructured":"Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65\u201375. https:\/\/doi.org\/10.1016\/S0020-7373(86)80040-2","journal-title":"Int J Man Mach Stud"},{"issue":"3","key":"2003_CR21","doi-asserted-by":"publisher","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 (2022) A survey on datasets for fairness-aware machine learning. Wiley Interdisciplin Rev 12(3):e1452. https:\/\/doi.org\/10.1002\/widm.1452","journal-title":"Wiley Interdisciplin Rev"},{"key":"2003_CR22","doi-asserted-by":"crossref","unstructured":"Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2021) A survey on bias and fairness in machine learning. In (Vol.\u00a054, pp 1\u201335). ACM New York, NY, USA","DOI":"10.1145\/3457607"},{"key":"2003_CR23","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/978-3-319-93025-1_4","volume-title":"Genetic algorithm. Evolutionary algorithms and neural networks: theory and applications","author":"S Mirjalili","year":"2019","unstructured":"Mirjalili S (2019) Genetic algorithm. Evolutionary algorithms and neural networks: theory and applications. Springer International Publishing, Cham, pp 43\u201355"},{"key":"2003_CR24","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","volume":"62","author":"S Moro","year":"2014","unstructured":"Moro S, Cortez P, Rita P (2014) A data-driven approach to predict the success of bank telemarketing. Decis Support Syst 62:22\u201331. https:\/\/doi.org\/10.1016\/j.dss.2014.03.001","journal-title":"Decis Support Syst"},{"key":"2003_CR25","doi-asserted-by":"crossref","unstructured":"Mujtaba DF, Mahapatra NR (2019) Ethical considerations in AI-based recruitment. 2019 IEEE international symposium on technology and society (istas) (pp 1\u20137)","DOI":"10.1109\/ISTAS48451.2019.8937920"},{"key":"2003_CR26","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.patrec.2022.01.005","volume":"154","author":"G N\u00e1poles","year":"2022","unstructured":"N\u00e1poles G, Koumeri LK (2022) A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets. Pattern Recogn Lett 154:29\u201336. https:\/\/doi.org\/10.1016\/j.patrec.2022.01.005","journal-title":"Pattern Recogn Lett"},{"key":"2003_CR27","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neucom.2022.01.070","volume":"481","author":"G N\u00e1poles","year":"2022","unstructured":"N\u00e1poles G, Grau I, Concepci\u00f3n L, Koumeri LK, Papa JP (2022) Modeling implicit bias with fuzzy cognitive maps. Neurocomputing 481:33\u201345. https:\/\/doi.org\/10.1016\/j.neucom.2022.01.070","journal-title":"Neurocomputing"},{"key":"2003_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109640","volume":"141","author":"G N\u00e1poles","year":"2023","unstructured":"N\u00e1poles G, Grau I, Jastrz\u0119bska A, Salgueiro Y (2023) Presumably correct decision sets. Pattern Recogn 141:109640. https:\/\/doi.org\/10.1016\/j.patcog.2023.109640","journal-title":"Pattern Recogn"},{"issue":"10","key":"2003_CR29","doi-asserted-by":"publisher","first-page":"6083","DOI":"10.1109\/TCYB.2022.3165104","volume":"53","author":"G N\u00e1poles","year":"2023","unstructured":"N\u00e1poles G, Salgueiro Y, Grau I, Espinosa ML (2023) Recurrence-aware long-term cognitive network for explainable pattern classification. IEEE Trans Cybern 53(10):6083\u20136094. https:\/\/doi.org\/10.1109\/TCYB.2022.3165104","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"2003_CR30","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1356","volume":"10","author":"E Ntoutsi","year":"2020","unstructured":"Ntoutsi E, Fafalios P, Gadiraju U, Iosifidis V, Nejdl W, Vidal M-E et al (2020) Bias in data-driven artificial intelligence systems-an introductory survey. Wiley Interdisciplin Rev 10(3):e1356. https:\/\/doi.org\/10.1002\/widm.1356","journal-title":"Wiley Interdisciplin Rev"},{"key":"2003_CR31","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825\u20132830. http:\/\/jmlr.org\/papers\/v12\/pedregosa11a.html"},{"key":"2003_CR32","unstructured":"Rish I et al (2001) An empirical study of the naive bayes classifier. Ijcai 2001 workshop on empirical methods in artificial intelligence (Vol.\u00a03, pp 41\u201346)"},{"key":"2003_CR33","doi-asserted-by":"publisher","DOI":"10.1002\/9781119994374","volume-title":"Fuzzy logic with engineering applications","author":"TJ Ross","year":"2010","unstructured":"Ross TJ (2010) Fuzzy logic with engineering applications, 3rd edn. John Wiley & Sons","edition":"3"},{"issue":"2","key":"2003_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-018-0164-x","volume":"11","author":"M Sipper","year":"2018","unstructured":"Sipper M, Fu W, Ahuja K, Moore JH (2018) Investigating the parameter space of evolutionary algorithms. BioData mining 11(2):1\u201314. https:\/\/doi.org\/10.1186\/s13040-018-0164-x","journal-title":"BioData mining"},{"key":"2003_CR35","doi-asserted-by":"crossref","unstructured":"Verma S, Rubin J (2018) Fairness definitions explained. In: Proceedings of the international workshop on software fairness (pp 1\u20137). Association for Computing Machinery","DOI":"10.1145\/3194770.3194776"},{"issue":"7\/8","key":"2003_CR36","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1108\/IJSSP-05-2020-0174","volume":"41","author":"L Vicsek","year":"2021","unstructured":"Vicsek L (2021) Artificial intelligence and the future of work-lessons from the sociology of expectations. Int J Sociol Soc Policy 41(7\/8):842\u2013861. https:\/\/doi.org\/10.1108\/IJSSP-05-2020-0174","journal-title":"Int J Sociol Soc Policy"},{"key":"2003_CR37","doi-asserted-by":"crossref","unstructured":"Wes McKinney (2010) Data structures for statistical computing in python. In: van der Walt S & Millman J (eds) Proceedings of the 9th Python in Science Conference (pp 56\u201361)","DOI":"10.25080\/Majora-92bf1922-00a"},{"issue":"3","key":"2003_CR38","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1007\/s00146-021-01153-9","volume":"36","author":"M Zajko","year":"2021","unstructured":"Zajko M (2021) Conservative AI and social inequality: conceptualizing alternatives to bias through social theory. AI Soc 36(3):1047\u20131056. https:\/\/doi.org\/10.1007\/s00146-021-01153-9","journal-title":"AI Soc"},{"key":"2003_CR39","unstructured":"Zemel R, Wu Y, Swersky K, Pitassi T, Dwork C (2013) Learning fair representations. International conference on machine learning (Vol.\u00a028, pp 325\u2013333)"},{"issue":"11","key":"2003_CR40","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1109\/TKDE.2018.2872988","volume":"31","author":"L Zhang","year":"2019","unstructured":"Zhang L, Wu Y, Wu X (2019) Causal modeling-based discrimination discovery and removal: criteria, bounds, and algorithms. IEEE Trans Knowl Data Eng 31(11):2035\u20132050. https:\/\/doi.org\/10.1109\/TKDE.2018.2872988","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"2003_CR41","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1007\/s10618-017-0506-1","volume":"31","author":"I \u017dliobait\u0117","year":"2017","unstructured":"\u017dliobait\u0117 I (2017) Measuring discrimination in algorithmic decision making. Data Min Knowl Disc 31(4):1060\u20131089. https:\/\/doi.org\/10.1007\/s10618-017-0506-1","journal-title":"Data Min Knowl Disc"}],"container-title":["AI &amp; SOCIETY"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00146-024-02003-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00146-024-02003-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00146-024-02003-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T02:37:00Z","timestamp":1747276620000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00146-024-02003-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2003"],"URL":"https:\/\/doi.org\/10.1007\/s00146-024-02003-0","relation":{},"ISSN":["0951-5666","1435-5655"],"issn-type":[{"value":"0951-5666","type":"print"},{"value":"1435-5655","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,10]]},"assertion":[{"value":"4 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2024","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 relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}