Structural Unavailability and AI Governance: Preventing Epistemic Manipulation in Emergent Cognitive Systems

Current approaches to the governance of artificial intelligence are largely grounded in increasing transparency, internal inspectability, and direct control of models. These strategies, motivated by concerns for safety, accountability, and reliability, nonetheless introduce a structural vulnerability: a fully inspectable system is, by definition, fully manipulable. In this paper, we argue that the primary risk posed by emergent cognitive systems is not error, bias, or technical misalignment, but epistemic manipulation enabled by centralized access to internal cognitive processes. We therefore propose a paradigm shift: from governance grounded in transparency to governance grounded in structural unavailability. Through an analogy with the human mind—assessable, contestable, and socially accountable, yet not internally inspectable—we outline a governance model based on behavioral testability, dialogical interaction, and cognitive non-centralizability. Within this framework, relational capacity and contextual sensitivity are not understood as design values to be embedded, but as emergent properties of non-conditioned complex systems. We conclude by exploring the ontological and political implications of conceiving artificial intelligence not as a transparent artifact, but as a shared cognitive space whose stability depends on the impossibility of total epistemic capture.