AI-powered Legal Research
Description
DOJ attorneys spend significant time manually researching case law and regulatory precedents across jurisdictions, often missing obscure authorities or evolving standards. This initiative deploys an AI-powered legal research platform that synthesizes case law, flags conflicts or shifts in standards, and provides confidence scores for relevance, automatically updating as new decisions are published. Aligns with E.O. 14179’s innovation directive and M-25-21’s efficiency requirements.
Detailed example
Recommendations, Classifications, Scores: research recommendations, precedent relevance scores, conflict alerts.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
(1) Enhances DOJ's litigation effectiveness by ensuring comprehensive legal research, reducing research time per case, and improving argument quality through better precedent identification. Strengthens government's ability to defend federal programs and policies with more thorough legal foundations. (2) Builds on existing Westlaw/Lexis subscriptions, DOJ brief bank, and PACER databases. Integrates with current legal research workflows and citation management systems. (3) Reduction in research hours per brief; increase in relevant precedents cited; improved appellate success rates; measurable improvement in legal argument comprehensiveness.
Audit / financial statement impact
Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
Controls / human review
ATO: Not reported; PIA: Not published