Resolves to bridge AI and digital divides and promote safe, secure, and trustworthy AI systems to achieve the Sustainable Development Goals. Encourages regulatory frameworks, capacity building, and international cooperation. Emphasizes human rights, data governance, and inclusive participation in AI governance.
Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the risk subdomain are explicitly described.
Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.
All other risk subdomains had no coverage in this document.
Risk Coverage Summary
The document has good coverage of approximately 10-12 subdomains, with strong focus on discrimination and bias (1.1, 1.3), privacy and data protection (2.1), misinformation (3.1, 3.2), malicious use (4.1, 4.2, 4.3), human agency (5.2), socioeconomic impacts (6.1, 6.2, 6.5), and system robustness (7.3, 7.4). Coverage is concentrated in human rights, governance, and equitable development domains.
Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the sector are described.
All other sectors had no coverage in this document.
Sectors Summary
This UN resolution does not govern specific economic sectors but rather establishes cross-cutting principles for AI governance applicable to all sectors. It is a horizontal governance framework focused on sustainable development, human rights, and international cooperation rather than sector-specific regulation. The document addresses AI systems broadly across all domains in the non-military sphere.
Soft Law
Legislative Status Summary
This is a UN General Assembly resolution that uses predominantly voluntary language ('encourages', 'invites', 'calls upon') without binding enforcement mechanisms or legal penalties for non-compliance. It establishes principles and recommendations for AI governance but relies on voluntary adherence by Member States.
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Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems
AI Lifecycle Summary
The document explicitly covers all AI lifecycle stages comprehensively, with particular emphasis on design, development, testing, deployment, and operation/monitoring. It repeatedly references 'throughout the life cycle of artificial intelligence systems' and explicitly lists all stages in its definition.
Terms relating to technical scope that are mentioned or implicitly referenced in the document
Technical Scope Summary
The document explicitly mentions AI systems throughout but does not define or distinguish between AI models and AI systems. It does not explicitly reference frontier AI, general purpose AI, task-specific AI, foundation models, generative AI, predictive AI, open-weight models, or compute thresholds. The focus is on 'safe, secure and trustworthy artificial intelligence systems' as a general category.
Document data from AGORA, CSET Emerging Technology Observatory.