Certifying Machine-Extracted Legal Logic
π‘See why legal AI logic can pass benchmarks yet fail when error rates transfer across chapters.
β‘ 30-Second TL;DR
What Changed
The system applies Monte Carlo replay of measured inter-extractor disagreement to the Duquenne-Guigues implication basis.
Why It Matters
The work offers a practical confidence layer for legal AI systems that convert statutes into machine-readable rules. Its results warn that reliability estimates can collapse when error rates are transferred across chapters or jurisdictions without local calibration.
What To Do Next
Before deploying a legal-rule extractor, implement per-chapter error calibration and require the Wilson survival certificate to meet the 0.95 threshold for every production implication.
Key Points
- β’The system applies Monte Carlo replay of measured inter-extractor disagreement to the Duquenne-Guigues implication basis.
- β’An implication is certified only when its one-sided Wilson 95% lower survival bound reaches 0.95, with premise spans and a minimal counterexample attached.
- β’Evaluation covered 29,365 Missouri sections and 502 Indian central-Act sections; 93.2% of held-out chapters fell below the informativeness floor under one global error model.
- β’A 2x2 factorial analysis attributed the failure primarily to calibration-rate transfer rather than dataset selection.
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Original source: ArXiv AI β
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