Curated information frameworks and the Governance of artificial intelligence

In this work, I introduce the Curated Information Framework (CIF) as a participatory model for governing artificial intelligence (AI) systems. I argue that meaningful AI governance must be understood as integrating practices of resistance to technological determinism, reclamation of community authority and sovereignty in technological use domains, and reimagining of a collaborative future rather than disclosure and use alone. Using agricultural AI as a central case, I show how prevailing transparency regimes often reinforce technocratic power by releasing information in forms that remain inaccessible or misaligned with the lived realities of those most affected. Drawing on participatory agricultural research and democratic epistemology, I reconceive transparency as epistemic curation: the role-specific, participatory, and iterative organization of information to support shared understanding across stakeholders. Through analysis of a case study involving an autonomous apple-picking system, I show how CIFs can redistribute interpretive authority among engineers, growers, farmworkers, and regulators, enabling communities not only to understand AI systems but to contest their terms of evaluation and deployment. I argue that such frameworks resist epistemic injustice, refuse extractive models of expertise, and reclaim governance authority for communities whose labor, land, and knowledge are entangled with AI systems. In doing so, I offer a practical and normative account of how AI governance can be re-imagined as a democratic, context-sensitive, and community-embedded process.