Quantifying Existential Cost: A Composite Framework for Determining Human Retention Thresholds in AI Decision-Making — With a Discussion of the "Decision Ontology" Turn in AI Governance

The governance of artificial intelligence faces a fundamental challenge: determining which decisions must retain human involvement. Existing risk-based frameworks primarily classify decisions according to application domain and potential harm severity, yet neglect a foundational question—the ontological characteristics inherent to the decisions themselves. Beyond their external consequences, decisions vary structurally in the existential costs they carry: the irreversible burdens that only beings with embodied mortality can bear. This paper advances the "existential cost" concept from an abstract philosophical thesis to an operational governance framework. We propose the Composite Cost Index (CCI), which operationalizes existential cost through five evaluable dimensions: irreversibility of consequences, temporal constraints, scope of affected parties, intensity of value conflicts, and estimated cognitive load. Based on weighted scoring across these dimensions, the CCI framework delineates three governance tiers—mandatory human retention, conditional delegation, and safe delegation—providing differentiated governance guidance for AI decisions at varying risk levels. Through four representative cases—autonomous vehicle emergency avoidance, AI-assisted medical diagnosis, content recommendation systems, and algorithmic sentencing assistance—we demonstrate the framework's analytical applications and address four potential theoretical objections. The framework's core contribution lies in transforming the normative question of "which decisions must be made by humans" from implicit intuitive judgment to explicit structured assessment, thereby establishing a new analytical tool and discussion platform for AI governance.