Verifiable Provenance for AI Judicial Decision Support Using Transparency Logs and Citation Entailment

Abstract We present VADP, an independently auditable systems-integration architecture for AI-assisted judicial decision support. Rather than proposing new cryptographic primitives, VADP contributes a domain-specific structural integration: binding Zero-Trust ABAC, Natural Language Inference (NLI) citation entailment, TreeSHAP feature attributions, and RFC 6962 transparency-log ledgers into a seven-field Verification Contract under conformal risk bounds, giving courts a checkable artifact anchoring evidentiary custody, authorization, and explainability (Section 63(4), Bharatiya Sakshya Adhiniyam, 2023). Empirical validation over a 1,500-query judicial benchmark yields a Mean Reciprocal Rank of 0.951 and Precision@1 of 0.931 . An NLI citation-entailment gate intercepts 94.4% of ungrounded claims and 98.0% of direct contradictions. The architecture demonstrates a 100.0% defense rate against prompt-injection (versus 11.0% undefended) and 100.0% tamper-detection across 10,000 contract corruptions. A conformal risk-control framework, calibrated on 500 appellate cases and evaluated on a disjoint 200-case test set, bounds automated escalation misses to 4.50% against a target risk of α = 0.05, using an appellate-outcome labeling rule that has not yet been independently audited by a legal-domain expert outside the authoring team. An initial same-split retraining experiment appeared to reach P@1 = 1.0000; a disjoint train/test protocol revealed this to be an overfitting artifact. Jurisdiction-specific retraining under a disjoint split yields a substantial out-of-distribution gain over baseline (P@1 0.482→0.684 SCOTUS, 0.441→0.612 ECtHR); these cross-jurisdictional figures remain below the in-domain benchmark (P@1 = 0.931), short of parity. Six infrastructure results (Hyperledger Fabric consensus, HSM/KMS key isolation, Groth16 ZK proof-of-concept) demonstrate single-host feasibility only; none has been evaluated under live, multi-organization deployment settings. The Groth16 component uses a single-operator trusted setup with no formal soundness guarantee — an engineering demonstration, not a security claim. The Verification Contract's explainability field (Field 4) attributes the retrieval re-ranker's ranking decisions via TreeSHAP; it does not explain the generative model's underlying legal reasoning, which remains outside this prototype's scope.

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Publication Details

Journal
Research Square
Published
2026-10-30
DOI
https://doi.org/10.21203/rs.3.rs-11040747/v1
Primary Topic
Artificial Intelligence in Law
Type
preprint
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preprint

Verifiable Provenance for AI Judicial Decision Support Using Transparency Logs and Citation Entailment

Aditya Mandloi, Anushka Yadav
Research Square
Artificial Intelligence in Law
preprint

Verifiable Provenance for AI Judicial Decision Support Using Transparency Logs and Citation Entailment

Aditya Mandloi, Anushka Yadav
preprint en

Abstract

Abstract We present VADP, an independently auditable systems-integration architecture for AI-assisted judicial decision support. Rather than proposing new cryptographic primitives, VADP contributes a domain-specific structural integration: binding Zero-Trust ABAC, Natural Language Inference (NLI) citation entailment, TreeSHAP feature attributions, and RFC 6962 transparency-log ledgers into a seven-field Verification Contract under conformal risk bounds, giving courts a checkable artifact anchoring evidentiary custody, authorization, and explainability (Section 63(4), Bharatiya Sakshya Adhiniyam, 2023). Empirical validation over a 1,500-query judicial benchmark yields a Mean Reciprocal Rank of 0.951 and Precision@1 of 0.931 . An NLI citation-entailment gate intercepts 94.4% of ungrounded claims and 98.0% of direct contradictions. The architecture demonstrates a 100.0% defense rate against prompt-injection (versus 11.0% undefended) and 100.0% tamper-detection across 10,000 contract corruptions. A conformal risk-control framework, calibrated on 500 appellate cases and evaluated on a disjoint 200-case test set, bounds automated escalation misses to 4.50% against a target risk of α = 0.05, using an appellate-outcome labeling rule that has not yet been independently audited by a legal-domain expert outside the authoring team. An initial same-split retraining experiment appeared to reach P@1 = 1.0000; a disjoint train/test protocol revealed this to be an overfitting artifact. Jurisdiction-specific retraining under a disjoint split yields a substantial out-of-distribution gain over baseline (P@1 0.482→0.684 SCOTUS, 0.441→0.612 ECtHR); these cross-jurisdictional figures remain below the in-domain benchmark (P@1 = 0.931), short of parity. Six infrastructure results (Hyperledger Fabric consensus, HSM/KMS key isolation, Groth16 ZK proof-of-concept) demonstrate single-host feasibility only; none has been evaluated under live, multi-organization deployment settings. The Groth16 component uses a single-operator trusted setup with no formal soundness guarantee — an engineering demonstration, not a security claim. The Verification Contract's explainability field (Field 4) attributes the retrieval re-ranker's ranking decisions via TreeSHAP; it does not explain the generative model's underlying legal reasoning, which remains outside this prototype's scope.

Research Square
Devi Ahilya Vishwavidyalaya (IN), IPS Academy (IN)
Peace, justice, and strong institutions
Artificial Intelligence in Law
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