Fairness-Preserving Implementation of Machine Learning Models

Fairness-Preserving Implementation of Machine Learning Models | 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 Research Article Fairness-Preserving Implementation of Machine Learning Models Shahadat Uddin, Haohui Lu, Farshid Hajati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6466737/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 Fairness in machine learning systems is essential for building trustworthy, ethical, and socially responsible AI, particularly in high-stakes domains such as healthcare and human services. This study proposes a comprehensive fairness-preserving framework integrating data bias quantification with model-level fairness evaluation and eliminating its violation. The framework uses Earth Mover’s Distance to quantify the distributional discrepancy between subgroups and the overall population, providing a statistical foundation for identifying group-level data bias. We assess fairness across five widely accepted definitions (i.e., demographic parity, equalised odds, equal opportunity, false positive rate parity, and predictive parity), each derived from the confusion matrix outcomes of ML models. The framework is empirically validated using a real-world health dataset and five commonly used supervised learning algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Results show that fairness-preserving adjustments, mainly through targeted data modification, significantly reduce fairness violations with minimal impact on overall model performance. By combining data-level bias quantification with robust statistical validation, this work offers a practical and interpretable approach to implementing fairness in ML systems. The framework lays a foundation for future extensions incorporating intersectional fairness, multi-class classification, and dynamic data environments. It contributes toward the development of AI systems that are not only accurate but also equitable and accountable. Artificial Intelligence and Machine Learning Machine Learning ML Fairness Earth Mover’s Distance Full Text Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6466737","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444066008,"identity":"c0b70223-c6f6-4f3e-ac0a-14a4c203a550","order_by":0,"name":"Shahadat Uddin","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0091-6919","institution":"The University of Sydney","correspondingAuthor":true,"prefix":"","firstName":"Shahadat","middleName":"","lastName":"Uddin","suffix":""},{"id":444066009,"identity":"8d7de9ba-024c-454c-a9d1-56d870422c39","order_by":1,"name":"Haohui Lu","email":"","orcid":"https://orcid.org/0000-0002-5338-156X","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Haohui","middleName":"","lastName":"Lu","suffix":""},{"id":444066010,"identity":"3d619c5a-6d3c-4d9b-8543-49f2e18ef801","order_by":2,"name":"Farshid Hajati","email":"","orcid":"https://orcid.org/0000-0002-8573-5297","institution":"University of New England","correspondingAuthor":false,"prefix":"","firstName":"Farshid","middleName":"","lastName":"Hajati","suffix":""}],"badges":[],"createdAt":"2025-04-16 23:56:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6466737/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6466737/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80864925,"identity":"249c15c9-4f3e-4585-ac0a-b3481ece2293","added_by":"auto","created_at":"2025-04-18 03:09:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":502070,"visible":true,"origin":"","legend":"","description":"","filename":"AFrameworkforFairnessv02.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6466737/v1_covered_0e3d4c5e-7f27-4417-9360-480f48778cc4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFairness-Preserving Implementation of Machine Learning Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Sydney","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":"Machine Learning, ML Fairness, Earth Mover’s Distance","lastPublishedDoi":"10.21203/rs.3.rs-6466737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6466737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFairness in machine learning systems is essential for building trustworthy, ethical, and socially responsible AI, particularly in high-stakes domains such as healthcare and human services. 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