{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:37:11Z","timestamp":1776811031564,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T00:00:00Z","timestamp":1648252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1717610"],"award-info":[{"award-number":["CCF-1717610"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MIT-IBM Watson AI Lab","award":["W1771646"],"award-info":[{"award-number":["W1771646"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information\u2013theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and is shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables that are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performance\u2013fairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).<\/jats:p>","DOI":"10.3390\/e24040461","type":"journal-article","created":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T21:27:51Z","timestamp":1648416471000},"page":"461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Maximal Correlation Framework for Fair Machine Learning"],"prefix":"10.3390","volume":"24","author":[{"given":"Joshua","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3479-4553","authenticated-orcid":false,"given":"Yuheng","family":"Bu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4435-0486","authenticated-orcid":false,"given":"Prasanna","family":"Sattigeri","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA 02139, USA"}]},{"given":"Rameswar","family":"Panda","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA 02139, USA"}]},{"given":"Gregory W.","family":"Wornell","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}]},{"given":"Leonid","family":"Karlinsky","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA 02139, USA"}]},{"given":"Rogerio","family":"Schmidt Feris","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA 02139, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Selbst, A.D., Boyd, D., Friedler, S.A., Venkatasubramanian, S., and Vertesi, J. 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