This paper examines the role of type-theoretic formal methods, including dependent types, refinement types, and proof-carrying code, in the governance of artificial intelligence systems deployed in regulated environments. It clarifies a recurring category error in both academic and industry discourse: the conflation of verification mechanisms with governance mechanisms. The analysis establishes that type-theoretic approaches function as verification substrates within governance frameworks, rather than as governance mechanisms in themselves. In governance-relevant decidable fragments, SMT-backed refinement systems and decidable dependent-type checks reduce operationally to solver-checkable constraints. Proof terms, derivation trees, SMT models, and certificates serve as evidence formats for replay-sufficient authorization artifacts. These methods therefore instantiate constraint-satisfaction techniques within a governance envelope rather than defining an independent governance paradigm. The paper outlines architectural integration patterns for applying type-theoretic validation to probabilistic AI outputs, including input pre-validation, post-validation before release or actuation, and type-directed regeneration. It further identifies governance functions that lie outside the scope of type systems alone, including policy authority, temporal binding of semantics, escalation and override, audit replay, provenance, and evidentiary standing. The work is intended to inform enterprises, regulators, and researchers evaluating formal methods for AI governance, and to provide a substrate-neutral framing of deterministic governance requirements. Technical disclosure of type-theoretic patterns for AI governance is established separately in an IP.com defensive publication, FERZ-DP-2026-001 / IPCOM000277413D. This paper presents conceptual and architectural analysis only and does not disclose additional implementation details.
Meyman et al. (Sun,) studied this question.