AI resistance and epistemic agency: toward a relational framework for democratic and human-centered AI governance

Abstract Artificial intelligence (AI) increasingly mediates communication, interpretation, decision-making, and knowledge production, raising questions about how computational systems acquire epistemic authority. At the same time, resistance to AI is emerging across educational, creative, infrastructural, professional, and civic settings. This conceptual paper examines what such resistance reveals about the distribution and contestability of epistemic authority in AI-mediated environments. Drawing on Science and Technology Studies, critical AI studies, AI ethics, political philosophy, and epistemic theory, it extends emerging accounts of AI-mediated epistemic capture by connecting them to epistemic agency, resistance, and governance . Epistemic capture is conceptualized as a gradual and relational process through which interpretive authority can become increasingly organized around computational systems and difficult to contest. The paper develops a typology encompassing quiet refusal, epistemic, infrastructural, educational, creative, and decolonial resistance, together with reclamation, while emphasizing that resistance is not inherently emancipatory or epistemically beneficial. Instead, resistance can operate diagnostically by revealing conditions under which epistemic agency becomes constrained. Building on this argument, the paper develops a relational epistemic governance framework integrating reflexivity, transparency, contestability, human adjudication, epistemic pluralism, democratic legitimacy, and sustainability. The framework shifts attention from governing AI systems alone toward the institutional organization of epistemic authority: who can interpret, challenge, adjudicate, and reshape AI-mediated knowledge, and under what conditions such authority remains democratically accountable.