The Loss of Admissible Intervention: Decision Space Closure Before Commitment in Human–AI Systems

This preprint introduces the concept of admissible intervention and identifies a structural boundary in human–AI systems: the point at which no independent alternative remains capable of meaningfully altering system trajectory. The paper argues that decision space closure can occur prior to both commitment and observable error, while the system continues to appear coherent and aligned. In such conditions, human oversight remains formally present but becomes structurally ineffective. This reframes AI governance from outcome validation toward preserving the conditions under which meaningful intervention remains possible — before the system crosses the commit boundary. This work builds on prior research within the General Boundary Architecture (GBA) framework and extends the analysis of decision boundaries by identifying a structural condition in which the space of admissible intervention collapses before commitment occurs, despite continued system coherence. DOI: 10.5281/zenodo.19776886