ReviewFrontiers in artificial intelligence2026
Society-oriented AI governance: a parallel layered model and multi-actor coordination framework.
Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The rapid and unrestricted public adoption of large language models and other openly accessible AI technologies has exposed a fundamental structural weakness in existing AI governance frameworks: the systematic underrepresentation of society as the ultimate recipient of AI's consequences. Most current frameworks treat societal impact as a downstream consideration rather than a foundational design criterion. This paper proposes society-oriented governance - the principle that societal reception and utilization of AI must serve as the primary design criterion for governance frameworks - as a structural response to this failure.To operationalize this principle, we develop two interconnected analytical frameworks: a governance development framework that replaces the sequential, hierarchical structure of a prior layered governance model with a parallel and dynamic model in which the technical, ethical, and social layers are developed simultaneously and in continuous mutual interaction; and a multiple-actors framework that maps stakeholders across the full lifecycle of AI utilization and proposes coordination mechanisms suited to both centralized and federated governance paradigms. A key finding is the structural distinction between the 4-actors model, applicable to professionally mediated AI deployment, and the 3-actors model, which describes the dominant pattern of openly accessible consumer AI where no professional intermediary exists. This distinction reveals the social layer as the most critical and most underdeveloped layer in current AI governance, and motivates concrete recommendations for strengthening it through socially responsive design standards, national-level regulatory obligations, and federated multi-stakeholder coordination. These contributions are grounded in AI governance theory - in governance design, institutional architecture, and stakeholder coordination - and are not proposed as a sociological theory of society.
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