Type-Theoretic Formal Methods in AI Governance: Verification Substrates Within Authorization Frameworks
Abstract
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 industries. It clarifies a recurring source of confusion 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 decidable fragments, refinement types reduce to satisfiability-modulo-theories (SMT) constraint checking, and dependent types reduce to constraint solving; proof terms and certificates serve as formats for replay-sufficient decision evidence. These methods are therefore shown to be implementations within the broader constraint-satisfaction design space, not independent governance paradigms. The paper outlines architectural integration patterns for applying type-theoretic validation to probabilistic AI outputs, including pre-validation, post-validation prior to 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, human escalation, audit replay, and legal admissibility. 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 as prior art 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.