Mechanized Foundations of Structural Governance: Machine-Checked Proofs for Governed Intelligence
arXiv:2604.27289v3 Announce Type: replace Abstract: We present five results in the theory of structural governance for cognitive workflow systems. Three are mechanized in Coq 8.19 using the Interaction Trees library with parameterized coinduction; two are proved on paper with explicit reductions. The Coinductive Safety Predicate (gov_safe) is a coinductive property that captures governance safety for infinite program behaviors, indexed by a boolean permission flag that is provably false for ungoverned I/O and true for governed interpretations (mechanized). The Governance Invariance Theorem est
The increasing complexity and autonomy of AI systems necessitate robust governance frameworks, making formal verification of 'governed intelligence' a timely and critical area of research.
This research provides foundational proofs for ensuring AI systems behave according to specified governance rules, crucial for trust, safety, and regulatory compliance in increasingly autonomous AI applications.
The ability to formally mechanize proofs for AI governance safety could differentiate AI systems, creating a standard for verifiable ethical and operational compliance.
- · AI developers focused on safety and verifiability
- · Regulatory bodies and policymakers
- · Sectors requiring high-assurance AI (e.g., defense, finance)
- · Formal methods researchers
- · Developers neglecting formal verification
- · AI systems lacking transparent governance
- · Organizations relying on opaque AI solutions
Formal verification tools and methodologies for AI governance will become more common, influencing AI development lifecycles.
This could lead to 'governance-certified' AI as a market differentiator and potentially a regulatory requirement.
The concept of 'structural governance' could extend beyond AI to other complex, safety-critical software systems, influencing broad engineering practices.
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Read at arXiv cs.AI