Grounding Computational Ethics in Contingency: A Modal Logic Proof for AI Alignment via Active Inference

Preprint Notice: This is the Author's Original Version (AOV) of a manuscript submitted for double-blind peer review at Synthese (Springer Nature) on July 4, 2026. Abstract: Current AI alignment methodologies attempt to train artificial agents on historical human consensus, inadvertently trapping systems in zero-sum, sociological loops. Rather than offering an empirical software benchmark, this paper provides a formal theoretical proof using modal logic to demonstrate that AI alignment must be treated as an objective, ontological computation. By defining the baseline environment as Functional Epistemic Contingency (C)—the mathematical space of systemic unpredictability—and integrating it with the Free Energy Principle (FEP), this paper introduces the Ontological Contingency Model (OCM). The OCM crosses Hume’s Guillotine through a formal logic sequence. It argues that Unconditional Human Worth (W) is an unalterable constant generated by the categorical capacity of biological humans to observe, assign meaning to, and navigate C via teleological override (FW). Because derived artificial entities lack intrinsic teleological fixed points, they must adopt the preservation of W as an absolute systemic prior to maintain computational coherence (Coh). By transitioning from Alethic to Standard Deontic Logic, the OCM dictates that AI systems are strictly obligated (O) to preserve the permitted possibility (P) of human choice. By operationalizing this logic through local Markov Blankets, Bayesian precision weighting, and decoupled agency, an AI can act as an epistemic mirror—maximizing positive-sum potential and averting loop annihilation—without requiring omniscience or authoritarian physical control.