Enhancing Fairness in Multi-Class Classification: A Post-Processing Approach with Linear Programming

Enhancing Fairness in Multi-Class Classification: A Post-Processing Approach with Linear Programming | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhancing Fairness in Multi-Class Classification: A Post-Processing Approach with Linear Programming Elizaveta Tarasova, Grigori Jasnovidov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7728769/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the contemporary landscape of AI and ML applications algorithmic fairness plays an important role. While extensive research has delved into fairness in binary classification and regression scenarios, the exploration of fairness in multi-class classification task has been relatively limited despite its potential usefulness in areas like credit scoring, school and university admission, criminal jurisdiction, etc. Indeed, in all these problems, the predicted label may take more than two values. The credit liability may be estimated as "low", "medium" and "high’"; the risk of recidivism may also have several values; the future performance of a student can be evaluated as a non-binary variable. In this paper, we present a post-processing type algorithm that increases fairness in multi-class classification problems. The core of our approach is a linear programming problem that allows our algorithm to relabel some predictions of the initial classifier in order to improve fairness with a small possible loss in accuracy. We observe decent performance of our algorithm on synthetic and real datasets COMPAS, LOAN, LSAC, ENEM, HSLS. The algorithm's general applicability and positive impact on fairness metrics position it as a valuable tool for mitigating fairness challenges in AI. Notably, our approach exhibits resilience even when confronted with non-optimal initial classifiers, reinforcing its practical utility and adaptability. Physical sciences/Mathematics and computing Social science/Science technology and society Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7728769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":533548899,"identity":"24cebc25-8512-4814-9a94-fbfd8de7ca01","order_by":0,"name":"Elizaveta Tarasova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYDACdsYGdCHmAwyYgsjyEFkJIAsmxJZAQAuEQtbCY4BXCz8zc/OHn202dfwN/Ac/V9TUyRkcP/P5c+GOwwzmMxKwapFsZmww7G1Lk5A4wMwseebYYWODM7nbpGeeOcwgcwO7FoPDjA0JPGcOSzAcYGaQbGA7kDizIXcbM2/bYQYJCexa7IFaDv45819CHmjLz4Z/dYkz+988/oxPiwEzY2MzT8UBCYMDzGySjW3Mif0SOQzS+LRIHGZsZpapSJbceJjZzLKx77Axv8QzM6Bf0nkkeB5gD7H29scf3xjY8csdb3x8s+FbnRwbf/JjYIhZy0mwY7cFAZjR2DwE1OPRPgpGwSgYBaMAAMa0WLQWfXaYAAAAAElFTkSuQmCC","orcid":"","institution":"Saint Petersburg State University","correspondingAuthor":true,"prefix":"","firstName":"Elizaveta","middleName":"","lastName":"Tarasova","suffix":""},{"id":533548900,"identity":"443e48e4-8e15-4809-9446-b603b90e55c9","order_by":1,"name":"Grigori Jasnovidov","email":"","orcid":"","institution":"ITMO University","correspondingAuthor":false,"prefix":"","firstName":"Grigori","middleName":"","lastName":"Jasnovidov","suffix":""}],"badges":[],"createdAt":"2025-09-27 13:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7728769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7728769/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94756063,"identity":"9bac0d87-bfe7-4d11-8664-f0b6b3e70ba3","added_by":"auto","created_at":"2025-10-30 11:09:28","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4056,"visible":true,"origin":"","legend":"","description":"","filename":"ee297959e1ca4ac695e5445bb9d0441f.json","url":"https://assets-eu.researchsquare.com/files/rs-7728769/v1/9deba3b92f32f6c52cfb2d91.json"},{"id":104393625,"identity":"39530194-5d80-49da-bd24-96ee3fbb25c5","added_by":"auto","created_at":"2026-03-11 10:42:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":702163,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsFairness.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7728769/v1_covered_706c573b-c9f3-4443-9c65-6912896057c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Fairness in Multi-Class Classification: A Post-Processing Approach with Linear Programming","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7728769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7728769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the contemporary landscape of AI and ML applications algorithmic fairness plays an important role. 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