eLitigation Tools

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Presumed high-impact, determined notSource: b) Presumed high-impact, but determined 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

2024-01 (month precision)Source: 01/2024

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Law Enforcement

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Addresses errors and speed delays found in exclusively manual human review of voluminous electronic information.

Expected benefits

Department of Justice components use electronic litigation (“eLitigation”) tools for a broad range of purposes, including support of investigations, litigation, and FOIA and Privacy Act processes. Most of these tools are commercial-off-the-self products that are commonly used outside of government, such as Everlaw, FOIAXpress, and Relativity. These tools increasingly integrate AI capabilities that can assist with tasks core to the mission of the Department, such as surfacing potentially discoverable information in voluminous collections of emails, text messages, or other electronic records; locating potentially inculpatory or exculpatory evidence in voluminous electronic data; and identifying material that may be appropriate for disclosure or withholding according to applicable legal rules and privileges. eLitigation tools can offer substantial benefits over exclusively human review of voluminous electronic information: they can be faster, more accurate and consistent, and more efficient. Please note: These tools are used in contexts that are high-impact, but the nature and details of AI uses vary, which may affect whether particular uses are high impact.

System outputs

Outputs vary by use case.

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)

Redacted for cybersecurity purposes.

Custom-developed code

No

Public source code

Not reported

Sourcing

How it was built

Purchased from vendorSource: a) Purchased from a vendor

Vendor (as reported)

CloudNine Law, Everlaw, Relativity, and Nuix.

Vendors (standardized)

RRelativityEEverlaw

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

Data and privacy

Involves PII

Yes

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

The case owner relied on DOJ AI governance practices to select and prepare data, as well as evaluate performance.

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