Neural Autoformalization Proves AI Law Compliance
AI converts messy laws/policies into machine-checkable logic via LLMs and symbolic solvers, enabling traceable decisions that regulators can verify in banking, healthcare, and data protection.
Policy-to-Logic Pipeline Delivers Verifiable Compliance
Neural autoformalization uses LLMs combined with neurosymbolic architectures to transform natural language policies—like "For loans above ₹10 lakh, at least two independent credit checks must be completed unless the customer is a government entity"—into precise formal rules: IF loan_amount > 1,000,000 AND customer_type ≠ GOVERNMENT THEN required_checks ≥ 2. This targets theorem provers (Lean, Coq, Isabelle), SMT solvers, and model checkers for machine verification.
The five-stage process starts by ingesting messy sources (PDFs, Word files) to segment into structured elements: definitions, obligations (must/shall), prohibitions, conditions (unless/only if), and thresholds (₹10 lakh, $10,000, 24 hours). LLMs generate candidate formalizations in SMT-LIB or temporal logic, respecting cross-references. Symbolic tools then verify consistency, simulate scenarios, and flag contradictions via redundant LLM translations. Human experts approve high-risk rules, creating a governed repository.
This compresses the risky chain (PDF → human interpretation → Excel → code → ML) into policy text → formal logic → enforced AI decisions, ensuring traceability (source clause), consistency (same input yields same output), and verifiability (prove decisions followed rules).
Driven by Regulation and AI Maturity in Key Sectors
Converging forces make this essential now: AI explosion in regulated fields (credit scoring, fraud/AML, claims triage, diagnostics); global rules like EU AI Act/GDPR, US executive orders, India DPDP Act demanding auditable compliance; and LLM advances in autoformalizing math proofs, now extending to policies.
In banking across US/EU/India/Global South, it formalizes KYC/AML thresholds, sanctions, and affordability rules from 180-page policies, auto-ingesting updates for versioned, jurisdiction-specific logic. AI blocks violating actions pre-commitment. Healthcare formalizes protocols (drug contraindications, sepsis escalations) so diagnostic AI proves guideline adherence. Data protection encodes GDPR/DPDP constraints as access/movement rules, preventing unauthorized cross-border flows in multi-cloud setups.
Outcomes: Regulators see exact rule traces for 10,000+ decisions; hospitals log evidence-backed plans; enterprises shift lawyers from manual coding to reviewing AI translations.
Enterprise Patterns, Risks, and Immediate Actions
Build a "Policy-to-Logic Factory": Ingest updates, prioritize high-impact sections (lending thresholds, data transfers), autoformalize, route for review, store in versioned repos. Expose as Guardrails-as-a-Service API: AI queries "Is this loan approval allowed?" with violation details if denied. Enable continuous audits via decision logs, rule tagging, and simulations (e.g., stricter EU thresholds).
Risks demand caution: Laws' intentional ambiguity resists rigid logic, risking false precision—formalize only checkable rules like thresholds, leave judicial parts human. Misformalization (dropped exceptions) cascades errors, so mandate redundant translations, SMT checks, and testing. Governance requires defining model ownership, review cadences, cross-jurisdiction conflict resolution.
Leaders map formalization gaps in critical policies, pilot one area (e.g., KYC) with stakeholders using LLMs/solvers in sandbox, then design approval workflows aligned to regulations before scaling. This evolves from "trust us" to "prove it," embedding non-negotiable constraints in AI agents for court-standable compliance.