Enabling eDiscovery Platform AI

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

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.

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Information Technology

Operational functions

Document processing and extraction, Claims and case processing Derived by keyword rules; see methodology

Problem the AI is intended to solve

Migrate CORA data to Relativity One or Everlaw, cloud based solutions to enable Relativity's and Everlaw's AI tools for review prioritization, privilege review and other advanced eDiscovery utilities. Utilize internal or third party-tools for AI-assisted collection and data processing, including image and voice recognition and analysis of complex data formats. Integrate E-Discovery tools into case management processes. This initiative transforms litigation support capabilities by leveraging AI to accelerate document review processes, reduce discovery costs, and improve case preparation efficiency and effectiveness.

Expected benefits

(1) Increases data availability and accessibility for integration with AI platforms through hosting on a scalable platform, in alignment with DOJ Data Strategy Goal #1 "Enterprise Data Management." Creates replicable enterprise capabilities that other DOJ components can adopt. (2) Allows migration to our existing environments more quickly. Discovery process will be streamlined with increased prioritization of review and lessens the time for manual privilege reviews. (3) AI enabled workflows available to Division litigating teams for privilege review, case strategy and prioritized document review.

System outputs

Prioritizations, Classifications, Recommendations: document priority rankings, privilege classifications, review recommendations.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported