{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:24:26Z","timestamp":1777728266765,"version":"3.51.4"},"reference-count":48,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IA"],"published-print":{"date-parts":[[2020,9,17]]},"abstract":"<jats:p>\u00a0Machine learning based systems and products are reaching society at large in many aspects of everyday life, including financial lending, online advertising, pretrial and immigration detention, child maltreatment screening, health care, social services, and education. This phenomenon has been accompanied by an increase in concern about the ethical issues that may rise from the adoption of these technologies. In response to this concern, a new area of machine learning has recently emerged that studies how to address disparate treatment caused by algorithmic errors and bias in the data. The central question is how to ensure that the learned model does not treat subgroups in the population unfairly. While the design of solutions to this issue requires an interdisciplinary effort, fundamental progress can only be achieved through a radical change in the machine learning paradigm. In this work, we will describe the state of the art on algorithmic fairness using statistical learning theory, machine learning, and deep learning approaches that are able to learn fair models and data representation.<\/jats:p>","DOI":"10.3233\/ia-190034","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T18:59:17Z","timestamp":1600801157000},"page":"151-178","source":"Crossref","is-referenced-by-count":2,"title":["Learning fair models and representations"],"prefix":"10.1177","volume":"14","author":[{"given":"Luca","family":"Oneto","sequence":"first","affiliation":[{"name":"DIBRIS, University of Genoa, 16145, Genova, Italy"},{"name":"ZenaByte s.r.l., www.zenabyte.com"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/IA-190034_ref1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10115-017-1116-3","article-title":"Auditing black-box models for indirect influence","volume":"54","author":"Adler","year":"2018","journal-title":"Knowledge and Information Systems"},{"issue":"3","key":"10.3233\/IA-190034_ref8","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10994-007-5040-8","article-title":"Convex multi-task feature learning","volume":"73","author":"Argyriou","year":"2008","journal-title":"Machine Learning"},{"issue":"2","key":"10.3233\/IA-190034_ref10","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1214\/009053606000001217","article-title":"Fast learning rates for plug-in classifiers","volume":"35","author":"Audibert","year":"2007","journal-title":"The Annals of Statistics"},{"issue":"Nov","key":"10.3233\/IA-190034_ref11","first-page":"463","article-title":"Rademacher and gaussian complexities: Risk bounds and structural results","volume":"3","author":"Bartlett","year":"2002","journal-title":"Journal of Machine Learning Research"},{"key":"10.3233\/IA-190034_ref12","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1613\/jair.731","article-title":"A model of inductive bias learning","volume":"12","author":"Baxter","year":"2000","journal-title":"Journal of Artificial Intelligence research"},{"issue":"1","key":"10.3233\/IA-190034_ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41060-016-0040-z","article-title":"Exposing the probabilistic causal structure of discrimination","volume":"3","author":"Bonchi","year":"2017","journal-title":"International Journal of Data Science and Analytics"},{"key":"10.3233\/IA-190034_ref19","unstructured":"Borwein J. , Lewis A.S. , Convex Analysis and Nonlinear Optimization: Theory and Examples, Springer (2010)."},{"issue":"2","key":"10.3233\/IA-190034_ref22","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s10618-010-0190-x","article-title":"Three naive bayes approaches for discrimination-free classification","volume":"21","author":"Calders","year":"2010","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"2","key":"10.3233\/IA-190034_ref29","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1089\/big.2016.0047","article-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments","volume":"5","author":"Chouldechova","year":"2017","journal-title":"Big Data"},{"key":"10.3233\/IA-190034_ref30","first-page":"134","article-title":"A case study of algorithmassisted decision making in child maltreatment hotline screening decisions","volume":"81","author":"Chouldechova","year":"2018","journal-title":"Proceedings of Machine Learning Research"},{"issue":"9","key":"10.3233\/IA-190034_ref42","first-page":"1342","article-title":"Fauw, J.R. 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