AI for Work: Using AI in legal and policy review

Using AI in Legal and Policy Review

What This Is

AI in legal and policy review automates the analysis of contracts, regulations, and compliance documents to flag risks, extract key clauses, and compare versions—saving hours of manual work. It matters because legal teams spend 30–50% of their time on repetitive document review (e.g., NDAs, GDPR compliance), and AI can reduce errors while freeing up experts for high-value tasks. Example: A corporate legal team uses AI to scan 1,000 vendor contracts in a day, identifying non-standard liability clauses that would take a paralegal weeks to flag manually.

Key Facts & Principles

• Document segmentation: AI breaks legal texts into logical parts (e.g., "Definitions," "Termination Clauses") for targeted analysis. Example: A tool like Kira Systems labels sections in a merger agreement to compare "Change of Control" provisions across drafts.
• Named Entity Recognition (NER): Identifies and classifies key terms (e.g., dates, parties, monetary values) in contracts. Example: Extracting all payment deadlines from a 50-page lease to create a compliance calendar.
• Semantic search: Finds relevant clauses based on meaning, not just keywords. Example: Searching for "force majeure" in a contract and retrieving clauses about "acts of God" or "unforeseen disruptions," even if those exact words aren’t used.
• Clause comparison: Highlights differences between document versions or benchmarks against standard templates. Example: Comparing a client’s NDA against your company’s standard template to flag deviations (e.g., a 5-year confidentiality term vs. your 2-year standard).
• Risk scoring: Assigns a numerical risk level to clauses based on predefined rules (e.g., "auto-flag any indemnification clause with unlimited liability"). Example: A tool scores a contract’s "Limitation of Liability" section as "High Risk" if it lacks a cap on damages.
• Regulatory mapping: Links policy documents to specific laws/regulations (e.g., GDPR Article 17-"Right to Erasure"). Example: An AI tool cross-references a company’s privacy policy with GDPR requirements to identify missing disclosures.
• Hallucination in legal AI: Models may invent case law or misstate statutes. Example: An AI cites a non-existent "California Data Privacy Act of 2023" as justification for a compliance requirement. Mitigation: Always verify outputs against primary sources (e.g., Westlaw, government websites).
• Prompt chaining: Breaking complex tasks into sequential prompts for accuracy. Example:
• "Extract all termination clauses from this contract."
• "Compare these clauses to our standard template and highlight deviations."
• "Flag any deviations that increase our risk exposure."
• Explainability: AI tools must show why they flagged a clause (e.g., "This clause violates Rule X because it lacks Y"). Example: A tool explains that a non-compete clause is unenforceable in California due to Business and Professions Code § 16600.
• Human-in-the-loop (HITL): AI suggests edits or flags, but a lawyer reviews and approves changes. Example: AI drafts a redline for a contract amendment, but a paralegal verifies the changes before sending to the client.

Step-by-Step Application

1. Define the scope
2. Identify the specific task (e.g., "Review 200 employment agreements for non-compete clauses" vs. "vague: ‘check contracts’").

3. Gather reference materials (e.g., standard templates, relevant laws, past redlines).

4. Choose the right tool

5. For contract review: Use specialized tools like Kira, Luminance, or Lawgeex (trained on legal data).
6. For regulatory mapping: Try ComplyAdvantage, OneTrust, or Casetext (links policies to laws).

7. For ad-hoc analysis: Use general-purpose AI (e.g., Claude, GPT-4) with clear prompts and retrieval-augmented generation (RAG) to pull in relevant laws.

8. Design the workflow

9. Input: Upload documents in a consistent format (e.g., PDFs with OCR, Word files).
10. Process:
    • Run NER to extract key terms (e.g., parties, dates, obligations).
    • Apply semantic search to find relevant clauses.
    • Compare against templates/laws and flag deviations.

11. Output: Generate a report with risk scores, redlines, or a compliance checklist.

12. Validate and refine

13. Spot-check: Manually review 5–10% of AI-flagged clauses to test accuracy.
14. Adjust prompts/rules: If the AI misses a common risk (e.g., "evergreen clauses"), update its training data or prompt logic.

15. Document exceptions: Note false positives/negatives (e.g., "AI flagged ‘confidentiality’ in a harmless context") to improve future runs.

16. Integrate with human review

17. Use AI for first-pass review, then route high-risk or ambiguous clauses to lawyers.

18. Example workflow:

    • AI flags 50 clauses in 100 contracts-Lawyer reviews the 10 highest-risk flags-Paralegal updates the contract management system.

19. Monitor and iterate

20. Track metrics: Time saved, error rates, false positives/negatives.
21. Update tools as laws change (e.g., new state privacy laws) or as your company’s risk tolerance evolves.

Common Mistakes

• Mistake: Using generic AI (e.g., ChatGPT) for legal review without fine-tuning or retrieval. Correction: Use legal-specific tools or augment general AI with RAG (e.g., upload your company’s contract templates and relevant laws). Why: Generic models lack domain-specific training and may misinterpret legal jargon (e.g., "consideration" in contracts vs. everyday language).

• Mistake: Assuming AI outputs are legally binding or compliant. Correction: Treat AI as a drafting assistant, not a substitute for legal advice. Always have a lawyer review final documents. Why: AI can hallucinate or misapply laws (e.g., suggesting a clause that’s unenforceable in a jurisdiction).

• Mistake: Ignoring document formatting (e.g., scanned PDFs, handwritten notes). Correction: Pre-process documents with OCR tools (e.g., Adobe Acrobat, Tesseract) to ensure text is machine-readable. Why: AI can’t analyze images or poorly scanned text, leading to missed clauses.

• Mistake: Over-relying on AI for subjective judgments (e.g., "Is this clause fair?"). Correction: Use AI for objective tasks (e.g., "Does this clause exist in our template?") and leave subjective calls to humans. Why: AI lacks nuanced understanding of business context or ethical trade-offs.

• Mistake: Not documenting AI’s limitations in workflows. Correction: Create a playbook outlining what AI can/can’t do (e.g., "AI can flag missing signatures but can’t verify notarization"). Why: Prevents over-trust and sets clear expectations for teams.

Practical Tips

• Start small: Pilot AI on a low-risk task (e.g., extracting dates from NDAs) before scaling to high-stakes reviews (e.g., M&A due diligence).
• Use "golden documents": Feed the AI 5–10 manually reviewed contracts to calibrate its risk scoring. Example: If your team always flags "unlimited liability" clauses, train the AI to prioritize those.
• Combine tools: Use Kira for clause extraction + Casetext for legal research + Excel for tracking to create a seamless workflow.
• Train your team: Hold a 30-minute session on how to prompt the AI (e.g., "Ask for sources" or "Compare to X law") and how to validate outputs.

Quick Practice Scenario

Scenario: Your company is updating its employee handbook to comply with a new state law requiring paid leave for bereavement. You’ve uploaded the current handbook and the new law to an AI tool. The AI suggests adding a clause: "Employees are entitled to 5 days of paid bereavement leave for the death of a family member, as defined by [State] Labor Code § 245.5."

Question: What’s the first step you should take before implementing this change?

Answer: Verify the AI’s citation by checking the actual text of Labor Code § 245.5 to confirm the law’s requirements (e.g., does it cover "family member" broadly or only immediate relatives?). Explanation: AI can hallucinate or misstate legal details—always cross-check with primary sources.

Last-Minute Cram Sheet

1. AI in legal review = automation for repetitive tasks (e.g., clause extraction, risk scoring), not legal advice.
2. NER (Named Entity Recognition) = AI’s way of finding key terms (dates, parties, $ amounts) in contracts.
3. Semantic search > keyword search for legal docs (finds "force majeure" even if the phrase isn’t used).
4. Prompt chaining = breaking tasks into steps (e.g., extract-compare-flag) for accuracy.
5. Hallucination = AI inventing laws/cases; mitigate by asking for sources or using RAG.
6. Human-in-the-loop (HITL) = AI suggests, humans decide (non-negotiable for legal work).
7. Risk scoring = numerical flags (e.g., "High Risk" for unlimited liability clauses).
8. OCR first if docs are scanned/handwritten—AI can’t read images.
9. Golden documents = manually reviewed files used to train/calibrate AI.
10. Always verify AI outputs against primary sources (e.g., Westlaw, government websites).