Robust and eXplainable artificial intelligence

Robust and eXplainable artificial intelligence | 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 Robust and eXplainable artificial intelligence Paolo Giudici, Emanuela Raffinetti, Marco Riani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3306884/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2024 Read the published version in International Journal of Data Science and Analytics → Version 1 posted 7 You are reading this latest preprint version Abstract Artificial Intelligence relies on the application of machine learning models which, while reaching high predictive accuracy, lack explainability and robustness. This is a problem in regulated industries, as authorities aimed at monitoring the risks arising from the application of AI methods may not validate them. No measurement methodologies are yet available to jointly assess accuracy, explainability and robustness of machine learning models. We propose a methodology which fills the gap, extending the forward search approach, employed in robust statistical learning, to machine learning models. Doing so, we will be able to evaluate, by means of interpretable statistical tests, whether a specific AI application is accurate, explainable and robust, by means of a unifying methodology. We apply our proposal to the context of bitcoin price prediction, comparing a linear regression model against a non linear neural network model. Bitcoin prices Explainable AI Robust AI Forward Search Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Feb, 2024 Read the published version in International Journal of Data Science and Analytics → Version 1 posted Editorial decision: Major revision 16 Oct, 2023 Reviews received at journal 13 Sep, 2023 Reviewers agreed at journal 06 Sep, 2023 Reviewers invited by journal 05 Sep, 2023 Editor assigned by journal 01 Sep, 2023 Submission checks completed at journal 30 Aug, 2023 First submitted to journal 29 Aug, 2023 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-3306884","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":229893270,"identity":"b3a2e349-b376-491d-9fe4-4be7fe0a3304","order_by":0,"name":"Paolo Giudici","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACZgYGiQQGCTkGBsYGIFeCeC3GMC3E6IGYnNiAxMEP5N15DG88qLFI33C7ue0xD4NFHUEthod5jC0SjknkbrhzsN2YhxiHGTbzmEkkNgC13Ehsk5xBipZ0A6K1yDNDtCSAtEh8IEaLATNbMcgvhjNvJLYbfDCQkGwgaEv/4Y03f9TUyfPdSH/2IKGijp+wLQcQbDYgl6AGoC1I7mAjQv0oGAWjYBSMRAAATYYz1aSySJAAAAAASUVORK5CYII=","orcid":"","institution":"University of Pavia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Giudici","suffix":""},{"id":229893272,"identity":"f4b0883e-76ef-4db9-9339-9dfb7738c905","order_by":1,"name":"Emanuela Raffinetti","email":"","orcid":"","institution":"University of Pavia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Emanuela","middleName":"","lastName":"Raffinetti","suffix":""},{"id":229893274,"identity":"db2797dd-e229-4ce0-abd6-dbcc5d6d77e8","order_by":2,"name":"Marco Riani","email":"","orcid":"","institution":"University of Parma","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Riani","suffix":""}],"badges":[],"createdAt":"2023-08-29 13:14:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3306884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3306884/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41060-024-00512-1","type":"published","date":"2024-02-16T15:01:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51323080,"identity":"286365dd-1102-4b75-862f-f1607e4a3a91","added_by":"auto","created_at":"2024-02-19 15:14:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":385987,"visible":true,"origin":"","legend":"","description":"","filename":"ExplainableRobust.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3306884/v1_covered_5c41793f-eaa9-4e72-996b-3620f912f1f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust and eXplainable artificial intelligence","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and Analytics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bitcoin prices, Explainable AI, Robust AI, Forward Search, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-3306884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3306884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence relies on the application of machine learning models which, while reaching high predictive accuracy, lack explainability and robustness. 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