{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T10:23:58Z","timestamp":1778408638262,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001961","name":"AXA Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001961","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10994-023-06401-1","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T17:01:45Z","timestamp":1695229305000},"page":"5081-5104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["How to be fair? A study of label and selection bias"],"prefix":"10.1007","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4676-5896","authenticated-orcid":false,"given":"Marco","family":"Favier","sequence":"first","affiliation":[]},{"given":"Toon","family":"Calders","sequence":"additional","affiliation":[]},{"given":"Sam","family":"Pinxteren","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Meyer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"issue":"1","key":"6401_CR1","first-page":"1086","volume":"26","author":"Y Ahn","year":"2019","unstructured":"Ahn, Y., & Lin, Y.-R. (2019). Fairsight: Visual analytics for fairness in decision making. IEEE Transactions on Visualization and Computer Graphics, 26(1), 1086\u20131095.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"6401_CR2","unstructured":"Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. http:\/\/www.fairmlbook.org."},{"issue":"4\/5","key":"6401_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1147\/JRD.2019.2942287","volume":"63","author":"RK Bellamy","year":"2019","unstructured":"Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovi\u0107, A., et al. (2019). AI fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development, 63(4\/5), 1\u20134.","journal-title":"IBM Journal of Research and Development"},{"key":"6401_CR4","doi-asserted-by":"crossref","unstructured":"Cabrera, \u00c1. A., Epperson, W., Hohman, F., Kahng, M., Morgenstern, J., & Chau, D. H. (2019). Fairvis: Visual analytics for discovering intersectional bias in machine learning. In: 2019 IEEE conference on visual analytics science and technology (VAST) (pp. 46\u201356). IEEE.","DOI":"10.1109\/VAST47406.2019.8986948"},{"key":"6401_CR5","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s10618-010-0190-x","volume":"21","author":"T Calders","year":"2010","unstructured":"Calders, T., & Verwer, S. (2010). Three Naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21, 277\u2013292.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"6401_CR6","unstructured":"Chen, I., Johansson, F. D., & Sontag, D. (2018). Why is my classifier discriminatory? Advances in Neural Information Processing Systems, 31."},{"key":"6401_CR7","doi-asserted-by":"crossref","unstructured":"Cooper, A. F., Abrams, E., & Na, N. (2021). Emergent unfairness in algorithmic fairness-accuracy trade-off research. In: Proceedings of the 2021 AAAI\/ACM conference on AI, ethics, and society (pp. 46\u201354).","DOI":"10.1145\/3461702.3462519"},{"key":"6401_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).","DOI":"10.1145\/3097983.3098095"},{"key":"6401_CR9","unstructured":"Dutta, S., Wei, D., Yueksel, H., Chen, P.-Y., Liu, S., & Varshney, K. (2020). Is there a trade-off between fairness and accuracy? A perspective using mismatched hypothesis testing. In: International conference on machine learning (pp. 2803\u20132813). PMLR."},{"key":"6401_CR10","doi-asserted-by":"crossref","unstructured":"Feldman, M., Friedler, S. A., 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).","DOI":"10.1145\/2783258.2783311"},{"key":"6401_CR11","unstructured":"Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016). On the (IM) possibility of fairness. arXiv:1609.07236."},{"issue":"4","key":"6401_CR12","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1145\/3433949","volume":"64","author":"SA Friedler","year":"2021","unstructured":"Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2021). The (IM) possibility of fairness: Different value systems require different mechanisms for fair decision making. Communications of the ACM, 64(4), 136\u2013143.","journal-title":"Communications of the ACM"},{"issue":"1","key":"6401_CR13","first-page":"473","volume":"29","author":"B Ghai","year":"2022","unstructured":"Ghai, B., & Mueller, K. (2022). D-bias: A causality-based human-in-the-loop system for tackling algorithmic bias. IEEE Transactions on Visualization and Computer Graphics, 29(1), 473\u2013482.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"1","key":"6401_CR14","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. Knowledge and Information Systems, 33(1), 1\u201333.","journal-title":"Knowledge and Information Systems"},{"key":"6401_CR15","unstructured":"Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv:1609.05807."},{"key":"6401_CR16","unstructured":"Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems, 30."},{"key":"6401_CR17","doi-asserted-by":"crossref","unstructured":"Lenders, D., & Calders, T. (2023). Real-life performance of fairness interventions-introducing a new benchmarking dataset for fair ML. In: Proceedings of the 38th ACM\/SIGAPP symposium on applied computing (pp. 350\u2013357).","DOI":"10.1145\/3555776.3577634"},{"key":"6401_CR18","unstructured":"Loftus, J. R., Russell, C., Kusner, M. J., & Silva, R. (2018). Causal reasoning for algorithmic fairness. arXiv:1805.05859."},{"issue":"6","key":"6401_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1\u201335.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"6401_CR20","unstructured":"Menon, A. K., & Williamson, R. C. (2018). The cost of fairness in binary classification. In: Conference on fairness, accountability and transparency (pp. 107\u2013118). PMLR."},{"issue":"6464","key":"6401_CR21","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447\u2013453. Publisher: American Association for the Advancement of Science.","journal-title":"Science"},{"key":"6401_CR22","unstructured":"Persoonsgegevens, A. (2021). Verwerking van persoonsgegevens in de fraude signale voorziening."},{"key":"6401_CR23","unstructured":"Ruf, B., & Detyniecki, M. (2021). Towards the right kind of fairness in AI. arXiv:2102.08453."},{"key":"6401_CR24","doi-asserted-by":"crossref","unstructured":"Ruf, B., & Detyniecki, M. (2022). A tool bundle for AI fairness in practice. In CHI Conference on human factors in computing systems extended abstracts (pp. 1\u20133).","DOI":"10.1145\/3491101.3519878"},{"key":"6401_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.clsr.2021.105567","volume":"41","author":"S Wachter","year":"2021","unstructured":"Wachter, S., Mittelstadt, B., & Russell, C. (2021). Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. Computer Law & Security Review, 41, 105567.","journal-title":"Computer Law & Security Review"},{"key":"6401_CR26","unstructured":"Wick, M. & Tristan, J.-B. (2019). Unlocking fairness: A trade-off revisited. Advances in Neural Information Processing Systems, 32."},{"key":"6401_CR27","unstructured":"Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013). Learning fair representations. In: International conference on machine learning, (pp. 325\u2013333). PMLR."},{"key":"6401_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, B. H., Lemoine, B., & Mitchell, M. (2018). Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI\/ACM conference on AI, ethics, and society. AIES \u201918 (pp. 335\u2013340). Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3278721.3278779.","DOI":"10.1145\/3278721.3278779"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06401-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-023-06401-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06401-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T00:02:50Z","timestamp":1726790570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-023-06401-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,20]]},"references-count":28,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["6401"],"URL":"https:\/\/doi.org\/10.1007\/s10994-023-06401-1","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,20]]},"assertion":[{"value":"15 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"the authors declare no conflict of interest outside their institution.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}