When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
Statutes are increasingly machine-parsed before humans read them, but different extractors disagree. This arXiv paper proposes a passive survival certificate for the Duquenne-Guigues implication basis of machine-extracted statutory contexts, using per-attribute disagreement, 1,000 Monte Carlo trials, and a one-sided Wilson 95% lower bound to certify implications. Experiments on Missouri and Indian statutes pass a preregistered held-out gate, yet a global error model makes 93.2% of held-out chapters fall below the informativeness floor, indicating the certificate is usable but fragile.
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[Submitted on 1 Sep 2026]
Title:When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
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Abstract:Statutes are increasingly parsed by machines before people read them, and the parsers disagree: on Missouri's statutes, two independently written extractors diverge on numeric-threshold presence at a false-negative rate of 0.43. We ask what formal logic survives such noise. We build a passive survival certificate for the Duquenne-Guigues implication basis of machine-extracted statutory contexts: per-attribute inter-extractor disagreement is measured, replayed against the basis in 1,000 Monte Carlo trials, and an implication is certified only when a one-sided Wilson 95% lower bound on survival reaches 0.95; every certified implication carries premise spans and a minimal counterexample. On 29,365 Missouri sections and 502 Indian central-Act sections, the preregistered held-out gate passes (10 statute families across 7 Titles exact; 16 across 11 with 5% tolerance), yet under one globally deployed error model 93.2% of held-out chapters fall below the informativeness floor, and a 2x2 factorial assigns that to calibration-rate transfer, not selection. The certificate is usable but fragile: deploy it per-chapter-calibrated or error-tolerant. Code, data products, and the audit trail, including one retracted claim, are released.
Comments: 18 pages, 9 figures, 13 tables (6 main text, 7 appendix). Code, data products, and preregistration to be released on GitHub
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
ACM classes: I.2.7; I.2.4
Cite as: arXiv:2609.01741 [cs.AI]
(or arXiv:2609.01741v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.01741
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Surya Saka [view email] [v1] Tue, 1 Sep 2026 18:08:01 UTC (72 KB)
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