A Redundancy-Adjusted Artificial Age Score Decision Support System for Oversight-Aware Human–Robot Collaboration

A Redundancy-Adjusted Artificial Age Score Decision Support System for Oversight-Aware Human–Robot Collaboration | 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 A Redundancy-Adjusted Artificial Age Score Decision Support System for Oversight-Aware Human–Robot Collaboration Seyma Yaman Kayadibi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9598452/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 Human–robot collaboration requires decision support systems that can evaluate safety-related burden and human oversight needs in shared operational environments. This paper proposes a redundancy-adjusted Artificial Age Score decision support system (AAS-DSS) for oversight-aware human–robot collaboration. Each event is represented through six normalized dimensions: task hazard severity, human safety risk, environmental uncertainty, task criticality, exposure frequency, and intervention complexity. These components are transformed into consistency-based burden indicators and aggregated through a redundancy-adjusted scoring structure. In this formulation, the Artificial Age Score does not measure chronological age or direct physical risk. Instead, it represents the structural decision burden of a human–robot collaboration event. The framework separates mandatory human review, triggered by direct safety indicators, from supervisory review, triggered by operational stress and monitoring conditions. Two illustrative warehouse robot scenarios demonstrate how the model can classify event burden and support oversight decisions. The resulting AAS-DSS is not an autonomous action selector, but an interpretable oversight-support layer. The proposed framework is intended as a configurable decision-support prototype whose weights, thresholds, and redundancy structure require domain-specific calibration before deployment. Artificial Age Score human–robot collaboration decision support system human oversight structural burden warehouse robotics AI safety 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. 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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-9598452","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633553887,"identity":"52e3a2ad-118b-4d8e-af52-23f9d6f2e15d","order_by":0,"name":"Seyma Yaman Kayadibi","email":"data:image/png;base64,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","orcid":"","institution":"Victoria University","correspondingAuthor":true,"prefix":"","firstName":"Seyma","middleName":"Yaman","lastName":"Kayadibi","suffix":""}],"badges":[],"createdAt":"2026-05-03 08:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9598452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9598452/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108892626,"identity":"903c462f-fb36-4133-8b05-ea643e3561b4","added_by":"auto","created_at":"2026-05-10 02:55:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":291888,"visible":true,"origin":"","legend":"","description":"","filename":"Archive.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9598452/v1_covered_de8961bf-6ed4-4029-83ab-e86e64bbd1b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Redundancy-Adjusted Artificial Age Score Decision Support System for Oversight-Aware Human–Robot Collaboration","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":"Artificial Age Score, human–robot collaboration, decision support system, human oversight, structural burden, warehouse robotics, AI safety","lastPublishedDoi":"10.21203/rs.3.rs-9598452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9598452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman–robot collaboration requires decision support systems that can evaluate safety-related burden and human oversight needs in shared operational environments. 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