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Society-oriented AI governance: a parallel layered model and multi-actor coordination framework
Aditya Firman Ihsan1, Thomhert Suprapto Siadari2, Andry Alamsyah3
1School of Computing, Telkom University, Bandung, Indonesia.
Frontiers in Artificial Intelligence
|July 23, 2026
Summary
Society-oriented governance is proposed to address AI governance weaknesses. This approach prioritizes societal impact in AI design, ensuring AI development benefits everyone by integrating social considerations from the start.
Area of Science:
- Artificial Intelligence Governance
- Societal Impact of AI
- AI Ethics and Policy
Background:
- Current AI governance frameworks inadequately represent society's role as the ultimate AI recipient.
- Societal impact is often treated as an afterthought, not a core design principle in AI development.
- Openly accessible AI technologies like large language models highlight these governance gaps.
Purpose of the Study:
- To propose society-oriented governance as a foundational principle for AI governance frameworks.
- To develop analytical frameworks for operationalizing society-oriented governance.
- To address the systematic underrepresentation of societal consequences in AI design.
Main Methods:
- Introduced a parallel and dynamic governance development framework, contrasting with prior sequential models.
- Developed a multiple-actors framework to map stakeholders across the AI lifecycle.
- Analyzed the distinction between 4-actor and 3-actor models for AI deployment scenarios.
Main Results:
- Identified the social layer as critical yet underdeveloped in current AI governance.
- Highlighted the structural difference between professionally mediated AI (4-actor model) and openly accessible AI (3-actor model).
- Demonstrated that societal reception must be the primary design criterion for AI governance.
Conclusions:
- Advocates for strengthening the social layer through socially responsive design standards and regulatory obligations.
- Recommends federated multi-stakeholder coordination for robust AI governance.
- Emphasizes AI governance theory, focusing on design, institutional architecture, and stakeholder coordination.
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