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Beyond the AI Divide: A Simple Approach to Identifying Global and Local Overperformers in AI Preparedness

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dc.contributor.authorMandon, Pierre
dc.date.accessioned2025-02-24T22:38:53Z
dc.date.available2025-02-24T22:38:53Z
dc.date.issued2025-02-24
dc.description.abstractThis paper examines global disparities in artificial intelligence preparedness, using the 2023 Artificial Intelligence Preparedness Index developed by the International Monetary Fund alongside the multidimensional Economic Complexity Index. The proposed methodology identifies both global and local overperformers by comparing actual artificial intelligence readiness scores to predictions based on economic complexity, offering a comprehensive assessment of national artificial intelligence capabilities. The findings highlight the varying significance of regulation and ethics frameworks, digital infrastructure, as well as human capital and labor market development in driving artificial intelligence overperformance across different income levels. Through case studies, including Singapore, Northern Europe, Malaysia, Kazakhstan, Ghana, Rwanda, and emerging demographic giants like China and India, the analysis illustrates how even resource-constrained nations can achieve substantial artificial intelligence advancements through strategic investments and coherent policies. The study underscores the need for offering actionable insights to foster peer learning and knowledge-sharing among countries. It concludes with recommendations for improving artificial intelligence preparedness metrics and calls for future research to incorporate cognitive and cultural dimensions into readiness frameworks.en
dc.identifierhttp://documents.worldbank.org/curated/en/099517502242572646/IDU15c1b47f8189d81406418b721329105034043
dc.identifier.doi10.1596/1813-9450-11073
dc.identifier.doihttps://doi.org/10.1596/1813-9450-11073
dc.identifier.urihttps://hdl.handle.net/10986/42856
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesPolicy Research Working Paper; 11073
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectAI PREPAREDNESS
dc.subjectECONOMIC COMPLEXITY
dc.subjectPEER LEARNING
dc.subjectPOLICY OVERPERFORMANCE
dc.titleBeyond the AI Divideen
dc.title.subtitleA Simple Approach to Identifying Global and Local Overperformers in AI Preparednessen
dc.typeWorking Paper
dspace.entity.typePublication
okr.associatedcontenthttps://reproducibility.worldbank.org/index.php/catalog/251 Link to reproducibility package
okr.date.disclosure2025-02-24
okr.date.doiregistration2025-04-14T11:52:30.325392Z
okr.date.lastmodified2025-02-24T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099517502242572646/IDU15c1b47f8189d81406418b721329105034043
okr.guid099517502242572646
okr.identifier.docmidIDU-5c1b47f8-89d8-4064-8b72-329105034043
okr.identifier.doi10.1596/1813-9450-11073
okr.identifier.externaldocumentum34459301
okr.identifier.internaldocumentum34459301
okr.identifier.reportWPS11073
okr.import.id6681
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099517502242572646/pdf/IDU15c1b47f8189d81406418b721329105034043.pdfen
okr.region.geographicalWorld
okr.topicMacroeconomics and Economic Growth::Economic Development
okr.topicGovernance::Governance Indicators
okr.topicScience and Technology Development::Technology Innovation
okr.unitEFI-AFR2-MTI-MacroFiscal-2 (EAWM2)
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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