{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T13:11:20Z","timestamp":1780319480744,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,1,27]],"date-time":"2019-01-27T00:00:00Z","timestamp":1548547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,1,27]]},"DOI":"10.1145\/3306618.3314255","type":"proceedings-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T12:10:59Z","timestamp":1562760659000},"page":"227-237","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Taking Advantage of Multitask Learning for Fair Classification"],"prefix":"10.1145","author":[{"given":"Luca","family":"Oneto","sequence":"first","affiliation":[{"name":"DIBRIS - University of Genoa, Genova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Doninini","sequence":"additional","affiliation":[{"name":"Istituto Italiano di Tecnologia, Genova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amon","family":"Elders","sequence":"additional","affiliation":[{"name":"Istituto Italiano di Tecnologia, Genova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimiliano","family":"Pontil","sequence":"additional","affiliation":[{"name":"Istituto Italiano di Tecnologia &amp; University College London, Genova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,1,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Conference on Fairness, Accountability, and Transparency in Machine Learning (FATML).","author":"Adebayo J.","unstructured":"J. Adebayo and L. Kagal . 2016. Iterative orthogonal feature projection for diagnosing bias in black-box models . In Conference on Fairness, Accountability, and Transparency in Machine Learning (FATML). J. Adebayo and L. Kagal. 2016. Iterative orthogonal feature projection for diagnosing bias in black-box models. In Conference on Fairness, Accountability, and Transparency in Machine Learning (FATML)."},{"key":"e_1_3_2_1_2_1","unstructured":"A. Agarwal A. Beygelzimer M. Dud'ik and J. Langford. 2017. A Reductions Approach to Fair Classification. In FATML. A. Agarwal A. Beygelzimer M. Dud'ik and J. Langford. 2017. A Reductions Approach to Fair Classification. In FATML."},{"key":"e_1_3_2_1_3_1","unstructured":"A. Agarwal A. Beygelzimer M. Dud'ik J. Langford and H. Wallach. 2018. A reductions approach to fair classification. arXiv preprint arXiv:1803.02453 (2018). A. Agarwal A. Beygelzimer M. Dud'ik J. Langford and H. Wallach. 2018. A reductions approach to fair classification. arXiv preprint arXiv:1803.02453 (2018)."},{"key":"e_1_3_2_1_4_1","unstructured":"D. Alabi N. Immorlica and A. T. Kalai. 2018. When optimizing nonlinear objectives is no harder than linear objectives. arXiv preprint arXiv:1804.04503 (2018). D. Alabi N. Immorlica and A. T. Kalai. 2018. When optimizing nonlinear objectives is no harder than linear objectives. arXiv preprint arXiv:1804.04503 (2018)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5040-8"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244304322765658"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622248.1622254"},{"key":"e_1_3_2_1_8_1","unstructured":"Y. Bechavod and K. Ligett. 2018. Penalizing Unfairness in Binary Classification. arXiv preprint arXiv:1707.00044v3 (2018). Y. Bechavod and K. Ligett. 2018. Penalizing Unfairness in Binary Classification. arXiv preprint arXiv:1707.00044v3 (2018)."},{"key":"e_1_3_2_1_9_1","unstructured":"R. Berk H. Heidari S. Jabbari M. Joseph M. Kearns J. Morgenstern S. Neel and A. Roth. 2017. A convex framework for fair regression. arXiv preprint arXiv:1706.02409 (2017). R. Berk H. Heidari S. Jabbari M. Joseph M. Kearns J. Morgenstern S. Neel and A. Roth. 2017. A convex framework for fair regression. arXiv preprint arXiv:1706.02409 (2017)."},{"key":"e_1_3_2_1_10_1","unstructured":"A. Beutel J. Chen Z. Zhao and E. H. Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. In FATML. A. Beutel J. Chen Z. Zhao and E. H. Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. In FATML."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_1_12_1","unstructured":"F. Calmon D. Wei B. Vinzamuri K. Natesan Ramamurthy and K. R. Varshney. 2017. Optimized Pre-Processing for Discrimination Prevention. In Advances in Neural Information Processing Systems (NIPS). F. Calmon D. Wei B. Vinzamuri K. Natesan Ramamurthy and K. R. Varshney. 2017. Optimized Pre-Processing for Discrimination Prevention. In Advances in Neural Information Processing Systems (NIPS)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"e_1_3_2_1_14_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data","author":"Chouldechova A.","year":"2017","unstructured":"A. Chouldechova . 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data , Vol. 5 , 2 ( 2017 ), 153--163. A. Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, Vol. 5, 2 (2017), 153--163."},{"key":"e_1_3_2_1_15_1","volume-title":"International Joint Conference on Neural Networks.","author":"Donini M.","unstructured":"M. Donini , D. Martinez-Rego , M. Goodson , J. Shawe-Taylor , and M. Pontil . 2016. Distributed variance regularized multitask learning . In International Joint Conference on Neural Networks. M. Donini, D. Martinez-Rego, M. Goodson, J. Shawe-Taylor, and M. Pontil. 2016. Distributed variance regularized multitask learning. In International Joint Conference on Neural Networks."},{"key":"e_1_3_2_1_16_1","unstructured":"M. Donini L. Oneto S. Ben-David J. Shawe-Taylor and M. Pontil. 2018. Empirical Risk Minimization under Fairness Constraints. In NIPS. M. Donini L. Oneto S. Ben-David J. Shawe-Taylor and M. Pontil. 2018. Empirical Risk Minimization under Fairness Constraints. In NIPS."},{"key":"e_1_3_2_1_17_1","volume-title":"Decoupled Classifiers for Group-Fair and Efficient Machine Learning. In Conference on Fairness, Accountability and Transparency (FAT).","author":"Dwork C.","year":"2018","unstructured":"C. Dwork , N. Immorlica , A. T. Kalai , and M. D. M. Leiserson . 2018 . Decoupled Classifiers for Group-Fair and Efficient Machine Learning. In Conference on Fairness, Accountability and Transparency (FAT). C. Dwork, N. Immorlica, A. T. Kalai, and M. D. M. Leiserson. 2018. Decoupled Classifiers for Group-Fair and Efficient Machine Learning. In Conference on Fairness, Accountability and Transparency (FAT)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014067"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_1_20_1","unstructured":"M. Hardt E. Price and N. Srebro. 2016. Equality of opportunity in supervised learning. In NIPS. M. Hardt E. Price and N. Srebro. 2016. Equality of opportunity in supervised learning. In NIPS."},{"key":"e_1_3_2_1_21_1","unstructured":"IBM. 2018. User-Manual CPLEX 12.7.1. IBM Software Group. IBM. 2018. User-Manual CPLEX 12.7.1. IBM Software Group."},{"key":"e_1_3_2_1_22_1","volume-title":"International Conference on Computer, Control and Communication.","author":"Kamiran F.","unstructured":"F. Kamiran and T. Calders . 2009. Classifying without discriminating . In International Conference on Computer, Control and Communication. F. Kamiran and T. Calders. 2009. Classifying without discriminating. In International Conference on Computer, Control and Communication."},{"key":"e_1_3_2_1_23_1","volume-title":"Machine Learning Conference.","author":"Kamiran F.","unstructured":"F. Kamiran and T. Calders . 2010. Classification with no discrimination by preferential sampling . In Machine Learning Conference. F. Kamiran and T. Calders. 2010. Classification with no discrimination by preferential sampling. In Machine Learning Conference."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.83"},{"key":"e_1_3_2_1_26_1","unstructured":"M. Kearns S. Neel A. Roth and Z. S. Wu. 2017. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. arXiv preprint arXiv:1711.05144 (2017). M. Kearns S. Neel A. Roth and Z. S. Wu. 2017. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. arXiv preprint arXiv:1711.05144 (2017)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33718-5_12"},{"key":"e_1_3_2_1_28_1","unstructured":"A. K. Menon and R. C. Williamson. 2018. The cost of fairness in binary classification. In FAT. A. K. Menon and R. C. Williamson. 2018. The cost of fairness in binary classification. In FAT."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401959"},{"key":"e_1_3_2_1_30_1","volume-title":"L. G\u00f3mez-Chova, and G. Camps-Valls.","author":"P\u00e9rez-Suay A.","year":"2017","unstructured":"A. P\u00e9rez-Suay , V. Laparra , G. Mateo-Garc'ia , J. Mu n oz-Mar'i , L. G\u00f3mez-Chova, and G. Camps-Valls. 2017 . Fair Kernel Learning. In Machine Learning and Knowledge Discovery in Databases . A. P\u00e9rez-Suay, V. Laparra, G. Mateo-Garc'ia, J. Mu n oz-Mar'i, L. G\u00f3mez-Chova, and G. Camps-Valls. 2017. Fair Kernel Learning. In Machine Learning and Knowledge Discovery in Databases."},{"key":"e_1_3_2_1_31_1","unstructured":"G. Pleiss M. Raghavan F. Wu J. Kleinberg and K. Q. Weinberger. 2017. On fairness and calibration. In NIPS. G. Pleiss M. Raghavan F. Wu J. Kleinberg and K. Q. Weinberger. 2017. On fairness and calibration. In NIPS."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"S. Shalev-Shwartz and S. Ben-David. 2014. Understanding machine learning: From theory to algorithms .Cambridge University Press. S. Shalev-Shwartz and S. Ben-David. 2014. Understanding machine learning: From theory to algorithms .Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"J. Shawe-Taylor and N. Cristianini. 2004. Kernel methods for pattern analysis .Cambridge University Press. J. Shawe-Taylor and N. Cristianini. 2004. Kernel methods for pattern analysis .Cambridge University Press.","DOI":"10.1017\/CBO9780511809682"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"A. J. Smola and B. Sch\u00f6lkopf. 2001. Learning with Kernels .MIT Press. A. J. Smola and B. Sch\u00f6lkopf. 2001. Learning with Kernels .MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"e_1_3_2_1_35_1","unstructured":"B. Woodworth S. Gunasekar M. I. Ohannessian and N. Srebro. 2017. Learning non-discriminatory predictors. In Computational Learning Theory. B. Woodworth S. Gunasekar M. I. Ohannessian and N. Srebro. 2017. Learning non-discriminatory predictors. In Computational Learning Theory."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Artificial Intelligence and Statistics.","author":"Zafar M. B.","unstructured":"M. B. Zafar , I. Valera , M. Gomez Rodriguez , and K. P. Gummadi . 2017. Fairness constraints: Mechanisms for fair classification . In International Conference on Artificial Intelligence and Statistics. M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi. 2017. Fairness constraints: Mechanisms for fair classification. In International Conference on Artificial Intelligence and Statistics."},{"key":"e_1_3_2_1_38_1","unstructured":"M. B. Zafar I. Valera M. Rodriguez K. Gummadi and A. Weller. 2017. From parity to preference-based notions of fairness in classification. In NIPS. M. B. Zafar I. Valera M. Rodriguez K. Gummadi and A. Weller. 2017. From parity to preference-based notions of fairness in classification. In NIPS."},{"key":"e_1_3_2_1_39_1","volume-title":"International Conference on Machine Learning.","author":"Zemel R.","unstructured":"R. Zemel , Y. Wu , K. Swersky , T. Pitassi , and C. Dwork . 2013. Learning fair representations . In International Conference on Machine Learning. R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork. 2013. Learning fair representations. In International Conference on Machine Learning."}],"event":{"name":"AIES '19: AAAI\/ACM Conference on AI, Ethics, and Society","location":"Honolulu HI USA","acronym":"AIES '19","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","AAAI American Association for Artificial Intelligence"]},"container-title":["Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3306618.3314255","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3306618.3314255","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:04Z","timestamp":1750208524000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3306618.3314255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,27]]},"references-count":39,"alternative-id":["10.1145\/3306618.3314255","10.1145\/3306618"],"URL":"https:\/\/doi.org\/10.1145\/3306618.3314255","relation":{},"subject":[],"published":{"date-parts":[[2019,1,27]]},"assertion":[{"value":"2019-01-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}