When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
Machine-extracted legal statutes suffer significant parser disagreement, with a false-negative rate of 0.43 on numeric-threshold detection across independent extractors The authors introduce a "survival certificate" for the Duquenne-Guigues implication basis, using Monte Carlo replay (1,000 trials) and a one-sided Wilson 95% lower bound to certify only implications surviving at ≥0.95 confidence Each certified implication is accompanied by premise spans and a minimal counterexample, enabling audi
Analysis
TL;DR
- Machine-extracted legal statutes suffer significant parser disagreement, with a false-negative rate of 0.43 on numeric-threshold detection across independent extractors
- The authors introduce a "survival certificate" for the Duquenne-Guigues implication basis, using Monte Carlo replay (1,000 trials) and a one-sided Wilson 95% lower bound to certify only implications surviving at ≥0.95 confidence
- Each certified implication is accompanied by premise spans and a minimal counterexample, enabling auditability of machine-extracted legal logic
- On 29,365 Missouri sections and 502 Indian central-Act sections, the certificate passes a preregistered held-out gate for 10 statute families across 7 Titles exactly, and 16 across 11 with 5% tolerance
- Under a globally deployed error model, 93.2% of held-out chapters fall below the informativeness floor, attributed to calibration-rate transfer rather than selection bias; the certificate is usable but fragile, requiring per-chapter calibration or error tolerance
Why It Matters
This work directly addresses the reliability gap in AI-driven legal text processing, where machine parsers increasingly mediate statutory interpretation before human review. For AI practitioners building legal NLP systems, it provides a formal, auditable framework for quantifying how much extracted logical structure can be trusted under realistic parser disagreement. The findings serve as a cautionary benchmark for anyone deploying automated legal logic extraction at scale.
Technical Details
- Survival Certificate Framework: A passive certification method for the Duquenne-Guigues implication basis derived from machine-extracted statutory contexts. Per-attribute inter-extractor disagreement is measured and replayed against the implication basis across 1,000 Monte Carlo trials.
- Certification Criterion: An implication is certified only when the one-sided Wilson 95% lower bound on survival probability reaches 0.95. Every certified implication includes premise spans and a minimal counterexample for traceability.
- Datasets: Evaluated on 29,365 Missouri statutory sections and 502 Indian central-Act sections, covering multiple statute families across 7+ Titles.
- Error Analysis: A 2x2 factorial experiment isolates the cause of informativeness failure, attributing the 93.2% failure rate under global error modeling to calibration-rate transfer rather than selection effects.
- Reproducibility: Code, data products, and full audit trail (including one retracted claim) are publicly released.
Industry Insight
- Legal AI systems should adopt per-chapter or per-domain calibration rather than relying on globally deployed error models, which the results show are insufficient for maintaining logical informativeness.
- The survival certificate approach offers a transferable template for certifying extracted logic in other structured-text domains (regulatory, contractual, clinical) where parser disagreement is inherent.
- Practitioners should treat machine-extracted legal logic as fragile by default; even passing a preregistered gate does not guarantee robustness under realistic error conditions, necessitating error-tolerant deployment strategies.
Disclaimer: The above content is generated by AI and is for reference only.