When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
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A research paper introduces a method to certify the reliability of machine-extracted legal logic from statutes, demonstrating high error rates in existing parsers and proposing a survival certificate framework. The work highlights fragility in automated statutory parsing, particularly in Missouri and Indian legal texts, with implications for AI systems relying on legal data.
- Article reports a 0.43 false-negative rate between two independent statutory parsers for Missouri's statutes
- 93.2% of held-out chapters fall below an informativeness floor under a globally deployed error model
- The proposed survival certificate framework is usable but requires per-chapter calibration or error tolerance
التأثير المتوقع على السوق
The findings may affect companies developing or deploying AI systems for legal document analysis, compliance, or regulatory technology (RegTech), particularly those using machine-extracted statutory logic. This could influence investor sentiment toward firms in AI-driven legal tech, such as those providing automated legal research or regulatory compliance tools.
المخاطر
- The research focuses on legal parsing accuracy and does not provide evidence of commercial deployment or market adoption by specific companies
- The fragility of the certificate under global error models suggests potential limitations in scalability for real-world applications
مسار الأدلة
الأدلة
مصدر التحليل بالذكاء الاصطناعي
المعرّفات التقنية
- وسم المزوّد
- mistral-small-latest
- إصدار التحليل
- mistral-small-latest
- معرّف المقال
- 126649
- الإطار الزمني
- 24h
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المصدر الأصلي
arXiv:2609.01741v1 Announce Type: new 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.
المقال الأصلي منشور بواسطة arXiv في 3 سبتمبر 2026. التحليل والرؤى المقدمة من AnalystMarkets AI.
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