Sovereignty-Preserving AI Systems and Mechanisms: A Survey
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2026
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| _version_ | 1866902036563361792 |
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| author | Liu, Qingfeng |
| author_facet | Liu, Qingfeng |
| contents | <p>This record contains a working-paper version of “Sovereignty-Preserving AI Systems and Mechanisms: A Survey.”</p> <p>The paper surveys technical and institutional mechanisms for preserving meaningful human and institutional control in AI-mediated environments. It organizes the literature across five layers of dependence: data, learning, action, exit, and ecosystem capacity. Mechanisms reviewed include on-device inference, federated adaptation, bounded agent architectures, machine unlearning, auditability, substitutability, and public compute.</p> <p>Using shared dimensions such as locality, participation in improvement, boundedness, reversibility, substitutability, and public verifiability, the survey argues that contemporary AI redistributes control across multiple technical boundaries rather than along a single axis such as safety or privacy. The resulting framework is intended to support the design and evaluation of governable AI systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19970390 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Sovereignty-Preserving AI Systems and Mechanisms: A Survey Liu, Qingfeng Keywords: Sovereignty-Preserving AI; Human Sovereignty; AI Governance; Autonomous Agents; Federated Learning; Machine Unlearning; On-Device Inference; Public Compute; Reversibility; Auditability Subjects: Artificial Intelligence; Computer Science; Technology and Society <p>This record contains a working-paper version of “Sovereignty-Preserving AI Systems and Mechanisms: A Survey.”</p> <p>The paper surveys technical and institutional mechanisms for preserving meaningful human and institutional control in AI-mediated environments. It organizes the literature across five layers of dependence: data, learning, action, exit, and ecosystem capacity. Mechanisms reviewed include on-device inference, federated adaptation, bounded agent architectures, machine unlearning, auditability, substitutability, and public compute.</p> <p>Using shared dimensions such as locality, participation in improvement, boundedness, reversibility, substitutability, and public verifiability, the survey argues that contemporary AI redistributes control across multiple technical boundaries rather than along a single axis such as safety or privacy. The resulting framework is intended to support the design and evaluation of governable AI systems.</p> |
| title | Sovereignty-Preserving AI Systems and Mechanisms: A Survey |
| topic | Keywords: Sovereignty-Preserving AI; Human Sovereignty; AI Governance; Autonomous Agents; Federated Learning; Machine Unlearning; On-Device Inference; Public Compute; Reversibility; Auditability Subjects: Artificial Intelligence; Computer Science; Technology and Society |
| url | https://doi.org/10.5281/zenodo.19970390 |