# When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic

arXiv: https://arxiv.org/abs/2609.01741 · HTML: https://mlsanity.com/paper/2609.01741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 3 of 3)

- [On the Usability of Probably Approximately Correct Implication Bases](https://mlsanity.com/paper/1701.00877/) (2017)
- [Interactive Error Correction in Implicative Theories](https://mlsanity.com/paper/1410.5215/) (2014)
- [LinCbO: fast algorithm for computation of the Duquenne-Guigues basis](https://mlsanity.com/paper/2011.04928/) (2020)

## Abstract (arXiv)

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.