Computational Law Governs AI Systems with Wolfram Language.

Governing AI systems, especially those with opaque reasoning, doesn't necessarily require understanding their internal logic. Instead, effective governance focuses on clearly defining what an AI system is obligated, permitted, or forbidden to do, and then verifying its compliance. This research introduces an implementation of Reified Input/Output Logic, a formalism underpinning the DAPRECO knowledge base, within Wolfram Language. This implementation covers core I/O axioms, obligations, permissions, constitutive norms, reified eventualities, and temporal operators. The study then explores the capability of GPT-4 to translate English legal statements into this formal computational law. The results reveal several shortcomings, including the hallucination of functions, omission of crucial temporal scopes, deviations from the specified formalism, and, in some cases, generating plausible-looking but fundamentally incorrect code that silently encodes the wrong norms. A practical case study involving an "AI guard dog" operating under a computational contract illustrates the potential. It shows how formalized rules can directly integrate into an embodied agent's operational code, enabling the AI to produce symbolic, auditable justifications for its actions. The author advocates for computational law as a powerful governance tool, suggesting that formalizing programmatically executable legal rules should be a key objective for future AI regulation.