{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:08:07Z","timestamp":1778825287704,"version":"3.51.4"},"reference-count":27,"publisher":"Maximum Academic Press","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"unspecified","delay-in-days":123,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Algorithmic bias arises in machine learning when models that may have reasonable overall accuracy are biased in favor of \u2018good\u2019 outcomes for one side of a sensitive category, for example gender or race. The bias will manifest as an\n                    <jats:italic>underestimation<\/jats:italic>\n                    of good outcomes for the under-represented minority. In a sense, we should not be surprised that a model might be biased when it has not been \u2018asked\u2019 not to be; reasonable accuracy can be achieved by ignoring the under-represented minority. A common strategy to address this issue is to include fairness as a component in the learning objective. In this paper, we consider including fairness as an additional criterion in model training and propose a multi-objective optimization strategy using Pareto Simulated Annealing that optimizes for both accuracy\n                    <jats:italic>and<\/jats:italic>\n                    underestimation bias. Our experiments show that this strategy can identify families of models with members representing different accuracy\/fairness tradeoffs. We demonstrate the effectiveness of this strategy on two synthetic and two real-world datasets.\n                  <\/jats:p>","DOI":"10.1017\/s0269888922000029","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T04:13:56Z","timestamp":1651637636000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":6,"title":["Using Pareto simulated annealing to address algorithmic bias in machine learning"],"prefix":"10.48130","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0079-2856","authenticated-orcid":false,"given":"William","family":"Blanzeisky","sequence":"first","affiliation":[]},{"given":"P\u00e1draig","family":"Cunningham","sequence":"additional","affiliation":[]}],"member":"27968","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"S0269888922000029_ref21","unstructured":"Kohavi, R. 1996. Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid. 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Oxford University Press."},{"key":"S0269888922000029_ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6940-7_15"},{"key":"S0269888922000029_ref7","doi-asserted-by":"publisher","DOI":"10.1029\/2019WR025852"},{"key":"S0269888922000029_ref20","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"S0269888922000029_ref9","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-1360(199801)7:1<34::AID-MCDA161>3.0.CO;2-6"},{"key":"S0269888922000029_ref1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8134674"},{"key":"S0269888922000029_ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278722"},{"key":"S0269888922000029_ref16","unstructured":"Hooker, S. , Moorosi, N. , Clark, G. , Bengio, S. & Denton, E. 2020. Characterising bias in compressed models. arXiv e-prints pp. arXiv\u20132010."},{"key":"S0269888922000029_ref24","doi-asserted-by":"crossref","unstructured":"Zafar, M. B. , Valera, I. , Gomez Rodriguez, M. & Gummadi, K. P. 2017. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web, 1171\u20131180.","DOI":"10.1145\/3038912.3052660"},{"key":"S0269888922000029_ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-73959-1_2"},{"key":"S0269888922000029_ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-93736-2_41"},{"key":"S0269888922000029_ref23","doi-asserted-by":"crossref","unstructured":"Pllana, S. , Memeti, S. & Kolodziej, J. 2019. Customizing Pareto simulated annealing for multi-objective optimization of control cabinet layout. In 2019 22nd International Conference on Control Systems and Computer Science (CSCS), 78\u201385.","DOI":"10.1109\/CSCS.2019.00021"},{"key":"S0269888922000029_ref6","unstructured":"Cotter, A. , Friedlander, M. P. , Goh, G. & Gupta, M. R. 2016. Satisfying real-world goals with dataset constraints. CoRR abs\/1606.07558."},{"key":"S0269888922000029_ref27","unstructured":"Zliobaite, I. 2017. Fairness-aware machine learning: A perspective. arXiv preprint arXiv:1708.00754."},{"key":"S0269888922000029_ref26","unstructured":"Zemel, R. , Wu, Y. , Swersky, K. , Pitassi, T. & Dwork, C. 2013. Learning fair representations. In Proceedings of the 30th International Conference on Machine Learning, Dasgupta, S. & McAllester, D. (eds). Proceedings of Machine Learning Research, 28, 325\u2013333. PMLR."},{"key":"S0269888922000029_ref13","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, 259\u2013268.","DOI":"10.1145\/2783258.2783311"},{"key":"S0269888922000029_ref4","unstructured":"Blanzeisky, W. , Cunningham, P. & Kennedy, K. 2021. Introducing a family of synthetic datasets for research on bias in machine learning. arXiv preprint arXiv:2107.08928."}],"container-title":["The Knowledge Engineering Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0269888922000029","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T14:42:22Z","timestamp":1767624142000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0269888922000029\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":27,"alternative-id":["S0269888922000029"],"URL":"https:\/\/doi.org\/10.1017\/s0269888922000029","relation":{},"ISSN":["0269-8889","1469-8005"],"issn-type":[{"value":"0269-8889","type":"print"},{"value":"1469-8005","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"\u00a9 The Author(s), 2022. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0\/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e5"}}