The sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic control
Abstract This study examines the sovereignty-internationalism paradox in AI governance, which encapsulates the tension between state control over algorithmic systems and the necessity of transnational collaboration to regulate borderless technologies. AI systems simultaneously reinforce national sov...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00374-x |
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| author | Artur Ishkhanyan |
| author_facet | Artur Ishkhanyan |
| author_sort | Artur Ishkhanyan |
| collection | DOAJ |
| description | Abstract This study examines the sovereignty-internationalism paradox in AI governance, which encapsulates the tension between state control over algorithmic systems and the necessity of transnational collaboration to regulate borderless technologies. AI systems simultaneously reinforce national sovereignty while demanding global cooperation, posing a fundamental challenge to governance frameworks. Through a comparative analysis of the European Union’s AI Act and China’s Social Credit System, this research explores how AI governance can either enable state authority or necessitate transnational coordination. The EU’s risk-based regulatory model demonstrates a supranational approach balancing innovation with ethical oversight, while China’s centralized AI-driven governance exemplifies state-centric control through algorithmic surveillance. To address these tensions, the study introduces Digital Federalism, a governance model that integrates subsidiarity and multi-tiered sovereignty, enabling regulatory coordination across local, national, and global levels. Unlike polycentric governance, which emphasizes decentralization, Digital Federalism retains hierarchical coordination while allowing for adaptive, context-sensitive governance. By bridging classical sovereignty theories with contemporary digital governance debates, this paper argues that reconciling national sovereignty and transnational cooperation requires adaptive frameworks prioritizing transparency, inclusivity, and accountability. The findings illuminate the interplay between state power and global governance, providing insights for policymakers navigating AI governance frameworks in an interconnected world. |
| format | Article |
| id | doaj-art-c882afb773d74cfda37a36d8c4d1a7cd |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-c882afb773d74cfda37a36d8c4d1a7cd2025-08-20T03:27:09ZengSpringerDiscover Artificial Intelligence2731-08092025-06-015111410.1007/s44163-025-00374-xThe sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic controlArtur Ishkhanyan0National Academy of Sciences of the Republic of ArmeniaAbstract This study examines the sovereignty-internationalism paradox in AI governance, which encapsulates the tension between state control over algorithmic systems and the necessity of transnational collaboration to regulate borderless technologies. AI systems simultaneously reinforce national sovereignty while demanding global cooperation, posing a fundamental challenge to governance frameworks. Through a comparative analysis of the European Union’s AI Act and China’s Social Credit System, this research explores how AI governance can either enable state authority or necessitate transnational coordination. The EU’s risk-based regulatory model demonstrates a supranational approach balancing innovation with ethical oversight, while China’s centralized AI-driven governance exemplifies state-centric control through algorithmic surveillance. To address these tensions, the study introduces Digital Federalism, a governance model that integrates subsidiarity and multi-tiered sovereignty, enabling regulatory coordination across local, national, and global levels. Unlike polycentric governance, which emphasizes decentralization, Digital Federalism retains hierarchical coordination while allowing for adaptive, context-sensitive governance. By bridging classical sovereignty theories with contemporary digital governance debates, this paper argues that reconciling national sovereignty and transnational cooperation requires adaptive frameworks prioritizing transparency, inclusivity, and accountability. The findings illuminate the interplay between state power and global governance, providing insights for policymakers navigating AI governance frameworks in an interconnected world.https://doi.org/10.1007/s44163-025-00374-xAI governanceSovereignty-internationalism paradoxDigital FederalismGlobal algorithmic controlTransnational cooperationAlgorithmic accountability |
| spellingShingle | Artur Ishkhanyan The sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic control Discover Artificial Intelligence AI governance Sovereignty-internationalism paradox Digital Federalism Global algorithmic control Transnational cooperation Algorithmic accountability |
| title | The sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic control |
| title_full | The sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic control |
| title_fullStr | The sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic control |
| title_full_unstemmed | The sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic control |
| title_short | The sovereignty-internationalism paradox in AI governance: digital federalism and global algorithmic control |
| title_sort | sovereignty internationalism paradox in ai governance digital federalism and global algorithmic control |
| topic | AI governance Sovereignty-internationalism paradox Digital Federalism Global algorithmic control Transnational cooperation Algorithmic accountability |
| url | https://doi.org/10.1007/s44163-025-00374-x |
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