{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T10:44:59Z","timestamp":1751712299675,"version":"3.37.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["952026","825619"],"award-info":[{"award-number":["952026","825619"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]},{"name":"WASP"},{"name":"Umea University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ethics Inf Technol"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The importance of fairness in machine learning models is widely acknowledged, and ongoing academic debate revolves around how to determine the appropriate fairness definition, and how to tackle the trade-off between fairness and model performance. In this paper we argue that besides these concerns, there can be ethical implications behind seemingly purely technical choices in fairness interventions in a typical model development pipeline. As an example we show that the technical choice between in-processing and post-processing is not necessarily value-free and may have serious implications in terms of who will be affected by the specific fairness intervention. The paper reveals how assessing the technical choices in terms of their ethical consequences can contribute to the design of fair models and to the related societal discussions.<\/jats:p>","DOI":"10.1007\/s10676-022-09636-z","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T17:02:52Z","timestamp":1646067772000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Ethical implications of fairness interventions: what might be hidden behind engineering choices?"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8423-8029","authenticated-orcid":false,"given":"Andrea","family":"Aler Tubella","sequence":"first","affiliation":[]},{"given":"Flavia","family":"Barsotti","sequence":"additional","affiliation":[]},{"given":"R\u00fcya G\u00f6khan","family":"Ko\u00e7er","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7383-0529","authenticated-orcid":false,"given":"Julian Alfredo","family":"Mendez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"9636_CR1","doi-asserted-by":"crossref","unstructured":"Binns, R. (2020). On the apparent conflict between individual and group fairness. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 514\u2013524.","DOI":"10.1145\/3351095.3372864"},{"issue":"2","key":"9636_CR2","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1089\/big.2016.0047","volume":"5","author":"A Chouldechova","year":"2017","unstructured":"Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153\u2013163.","journal-title":"Big Data"},{"key":"9636_CR3","unstructured":"Chouldechova, A., & Roth, A. (2018). The frontiers of fairness in machine learning. arXiv arXiv:1810.08810"},{"key":"9636_CR4","unstructured":"Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:180800023"},{"key":"9636_CR5","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1145\/3097983.3098095","volume":"F1296","author":"S Corbett-Davies","year":"2017","unstructured":"Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017). Algorithmic decision making and the cost of fairness. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol Part, F1296, 797\u2013806. https:\/\/doi.org\/10.1145\/3097983.3098095","journal-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol Part"},{"key":"9636_CR6","unstructured":"Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J., Pontil, M. (2018). Empirical risk minimization under fairness constraints. In Advances in Neural Information Processing Systems, Neural information processing systems foundation (Vol. 2018-December, pp. 2791\u20132801). arXiv:1802.08626"},{"key":"9636_CR7","doi-asserted-by":"publisher","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In ITCS 2012 - Innovations in Theoretical Computer Science Conference ACM Press, New York, New York, USA, pp. 214\u2013226. https:\/\/doi.org\/10.1145\/2090236.2090255, http:\/\/dl.acm.org\/citation.cfm?doid=2090236.2090255.","DOI":"10.1145\/2090236.2090255"},{"key":"9636_CR8","unstructured":"EBA. (2020). EBA report on big data and advanced analytics. European Banking Authority: Tech. rep."},{"key":"9636_CR9","unstructured":"EC. (2019). Ethics guidelines for trustworthy AI. European Commission: Tech. rep."},{"key":"9636_CR10","unstructured":"Green, B., & Hu, L. (2018). The myth in the methodology: Towards a recontextualization of fairness in machine learning. In Proceedings of the machine learning: the debates workshop."},{"key":"9636_CR11","unstructured":"Haas, C. (2020). The price of fairness\u2014A framework to explore trade-offs in algorithmic fairness. In 40th International Conference on Information Systems, ICIS 2019, Association for Information Systems."},{"key":"9636_CR12","unstructured":"Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems, pp. 3323\u20133331"},{"key":"9636_CR13","doi-asserted-by":"publisher","unstructured":"Holstein, K., Wortman Vaughan, J., Daum\u00e9, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery. New York, NY, USA, CHI \u201919, p 1\u201316, https:\/\/doi.org\/10.1145\/3290605.3300830","DOI":"10.1145\/3290605.3300830"},{"key":"9636_CR14","unstructured":"Joseph, M., Kearns, M.J., Morgenstern, J.H., & Roth, A. (2016). Fairness in learning: Classic and contextual bandits. In NIPS."},{"key":"9636_CR15","doi-asserted-by":"publisher","unstructured":"Kamiran, F., Karim, A., & Zhang, X. (2012). Decision theory for discrimination-aware classification. In Proceedings\u2014IEEE International Conference on Data Mining, ICDM, pp.\u00a0924\u2013929. https:\/\/doi.org\/10.1109\/ICDM.2012.45.","DOI":"10.1109\/ICDM.2012.45"},{"key":"9636_CR16","unstructured":"Kearns, M., Neel, S., Roth, A., & Wu, Z.S. (2018). Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International Conference on Machine Learning, PMLR, pp.\u00a02564\u20132572."},{"key":"9636_CR17","doi-asserted-by":"crossref","unstructured":"Kim, M., Ghorbani, A., & Zou, J. (2019). Multiaccuracy: Black-box post-processing for fairness in classification. In Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 247\u2013254.","DOI":"10.1145\/3306618.3314287"},{"key":"9636_CR18","unstructured":"Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:160905807."},{"key":"9636_CR19","unstructured":"Komiyama, J., Takeda, A., Honda, J., & Shimao, H. (2018). Nonconvex optimization for regression with fairness constraints. In 35th International Conference on Machine Learning, ICML 2018, PMLR (Vol .6, pp. 4280\u20134294). http:\/\/proceedings.mlr.press\/v80\/komiyama18a.html."},{"key":"9636_CR20","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A survey on bias and fairness in machine learning. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing."},{"key":"9636_CR21","doi-asserted-by":"crossref","unstructured":"Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling lime and shap: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society, pp.\u00a0180\u2013186.","DOI":"10.1145\/3375627.3375830"},{"key":"9636_CR22","unstructured":"Zafar, M.B., Valera, I., Gomez-Rodriguez, M., & Gummadi, K.P. (2019). Fairness constraints: A flexible approach for fair classification. Tech. rep., Max Planck Institute for Software Systems, http:\/\/fate-computing.mpi-sws.org\/."},{"key":"9636_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., & Mitchell, M. (2018). Mitigating unwanted biases with adversarial learning. In AIES 2018\u2014Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, pp.\u00a0335\u2013340, arXiv:1801.07593.","DOI":"10.1145\/3278721.3278779"}],"container-title":["Ethics and Information Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10676-022-09636-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10676-022-09636-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10676-022-09636-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T19:17:51Z","timestamp":1647976671000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10676-022-09636-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["9636"],"URL":"https:\/\/doi.org\/10.1007\/s10676-022-09636-z","relation":{},"ISSN":["1388-1957","1572-8439"],"issn-type":[{"type":"print","value":"1388-1957"},{"type":"electronic","value":"1572-8439"}],"subject":[],"published":{"date-parts":[[2022,2,28]]},"assertion":[{"value":"20 January 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"12"}}