aiqbench analysis
This research proposes using formalized computational law in Wolfram Language as a governance mechanism for opaque AI systems, shifting focus from inspecting internal reasoning to verifying compliance with executable obligations. The study demonstrates that while LLMs like GPT-4 struggle to accurately translate natural language legal norms into this specific logic without hallucinating functions or omitting temporal scopes, the approach successfully enables auditable justifications for embodied agents' actions. This work is critical for advancing AI safety by establishing a framework where regulatory constraints are programmatically enforced rather than merely interpreted.
Implementing Computational Law in Wolfram Language for the Governance of Artificial Intelligence
arXiv:2608.13958v1 Announce Type: new Abstract: How do we govern AI systems whose reasoning we cannot fully inspect? Governance does not require understanding a system's reasoning. It requires stating what the system is obliged, permitted, and forbidden to do, and checking whether it complied. I present an implementation of Reified Input/Output Logic, the formalism behind the DAPRECO knowledge base, in Wolfram Language: the core I/O axioms, obligations, permissions, constitutive norms, reified eventualities, and temporal operators. I then test whether GPT-4 can translate English legal statements into the formalism, and report the failures: hallucinated functions, omitted temporal scope, deviation from the formalism, and (in the worst cases) code that runs, reads plausibly, but silently encodes the wrong norm. A case study, an AI guard dog operating under a computational contract, shows how formalized rules can extend from a contract directly into the operational code of an embodied agent, producing symbolic, auditable justifications for its behaviour. I argue that computational law can be used as a governance tool and that a desirable goal would be to formalize the law that can and ought to be programmatically executable.
Abstract:How do we govern AI systems whose reasoning we cannot fully inspect? Governance does not require understanding a system's reasoning. It requires stating what the system is obliged, permitted, and forbidden to do, and checking whether it complied. I present an implementation of Reified Input/Output Logic, the formalism behind the DAPRECO knowledge base, in Wolfram Language: the core I/O axioms, obligations, permissions, constitutive norms, reified eventualities, and temporal operators. I then test whether GPT-4 can translate English legal statements into the formalism, and report the failures: hallucinated functions, omitted temporal scope, deviation from the formalism, and (in the worst cases) code that runs, reads plausibly, but silently encodes the wrong norm. A case study, an AI guard dog operating under a computational contract, shows how formalized rules can extend from a contract directly into the operational code of an embodied agent, producing symbolic, auditable justifications for its behaviour. I argue that computational law can be used as a governance tool and that a desirable goal would be to formalize the law that can and ought to be programmatically executable.
25 pages, 1 figure, 2 tables, 12 code listings. Wolfram Language implementation and verification script (31 assertions) available at this https URL
Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Logic in Computer Science (cs.LO)
I.2.4; F.4.1; K.4.1
arXiv:2608.13958 [cs.AI]
(or arXiv:2608.13958v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.13958 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 307 words
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