AI Evidence and Claim Consolidation
Description
This initiative applies AI to synthesize records, summarize expert reports and depositions, and identify duplicate claims. It also uses AI to identify inconsistencies between records and plaintiff claims, identify red flag legal issues, and create templates to respond to frequent or high-volume litigation. It also allows for analysis of settlement and damages databases to identify outlier trends. It supports M-25-21’s public trust pillar and E.O. 14179’s innovation agenda.
Detailed example
Summaries, Classifications, Predictions: document summaries, duplicate detection, inconsistency flagging, settlement predictions.
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) Improves the government’s litigation posture in high-value torts, reduces exposure to excessive payouts, and ensures equitable and efficient claims processing.(2) Builds on DOJ medical record review systems and HHS/VA data integration, along with Relativity/CORA settlement, damages, and entitlement databases. (3) Faster evidence review; detection and elimination of duplicate claims; creating more effective and consistent settlements; increased dismissal or settlement of weak claims; attorney time saved.
Controls / human review
ATO: Not reported; PIA: Not published