Regulatory Compliance and Bias Risk in AI Surveillance: A Case Study of Plate Recognition Under SCRAM

Regulatory Compliance and Bias Risk in AI Surveillance: A Case Study of Plate Recognition Under SCRAM | 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 Regulatory Compliance and Bias Risk in AI Surveillance: A Case Study of Plate Recognition Under SCRAM Kadir Kesgin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6498223/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 As artificial intelligence systems increasingly govern public safety operations, concerns over algorithmic fairness and legal compliance intensify. This study introduces a scenario-based evaluation framework (SCRAM) that simultaneously measures regulatory conformity and bias risks in AI-enabled surveillance. Using license plate recognition (LPR) systems in Türkiye as a case study, we simulate multiple operational configurations that vary decision thresholds and data retention periods. Each configuration is assessed through fairness metrics (Statistical Parity Difference, Disparate Impact Ratio) and a compliance score derived from KVKK (Türkiye’s Personal Data Protection Law) and constitutional jurisprudence. Our findings show that technical performance does not guarantee normative acceptability: several configurations with high detection accuracy fail to meet legal and fairness thresholds. The SCRAM model offers a modular and adaptable approach to align AI deployments with ethical and legal standards and highlights how policy-sensitive parameters critically shape risk landscapes. We conclude with implications for real-time audit systems and cross-jurisdictional AI governance. License Plate Recognition Algorithmic Fairness KVKK Compliance SCRAM Framework AI Surveillance 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. 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-6498223","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446160880,"identity":"88d9b706-b234-427c-8883-8026aacece76","order_by":0,"name":"Kadir Kesgin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACCQYGgwMMFQeAzMQGiNABorScQdXC2EBICwNjG0hLAgNxWiTbD288XDnvjrw5e3Lbpxs1DHJ8NxLYH1fg0SLNk1Zw8Oy2Z4Y7ex42z845xmAseSOBsfEMHi1yDDkGBxu3HWbccCOxmTmHjSFxA0gLPpfJ8b8Baplz2B6i5R9DPUEt0hIgWxoOJ4K15LYxJBgQ0iI541nBwYZjz5I3nHkI1NInYTjzzMPGmfi0SJxP3vyxoeaO7Ybj6Y+Zc77ZyPMdTz7wEZ8WDCOAGH+0jIJRMApGwSggAgAAJb1cVt5uI7wAAAAASUVORK5CYII=","orcid":"","institution":"Bandırma Onyedi Eylül University","correspondingAuthor":true,"prefix":"","firstName":"Kadir","middleName":"","lastName":"Kesgin","suffix":""}],"badges":[],"createdAt":"2025-04-21 18:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6498223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6498223/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86056028,"identity":"fcaca2b2-2fe5-415b-aefc-5bb7062a9883","added_by":"auto","created_at":"2025-07-05 06:08:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421610,"visible":true,"origin":"","legend":"","description":"","filename":"scramsprin.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6498223/v1_covered_f4edcbff-1ea9-440c-8786-134f1bab7625.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Regulatory Compliance and Bias Risk in AI Surveillance: A Case Study of Plate Recognition Under SCRAM","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"License Plate Recognition, Algorithmic Fairness, KVKK Compliance, SCRAM Framework, AI Surveillance","lastPublishedDoi":"10.21203/rs.3.rs-6498223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6498223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs artificial intelligence systems increasingly govern public safety operations, concerns over algorithmic fairness and legal compliance intensify. 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