DOJ · DOJ-0063 · record R1529

LPR: Federal Partner

Department of Justice · Department of Justice / DEA

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: High-impactSource: a) High-impact
Impact justification: Not reported
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

DEA needs an efficient and effective way to identify and track the movements of persons of interest based on vehicle license plates.

Expected benefits

License Plate Readers (LPR) can be one important investigative tool to support cases.

System outputs

This License Plate Reader (LPR) technology captures images of vehicle license plates, automatically converts the information they contain to characters, and can add metadata such as the time and location the image was captured.

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): 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: Yes
Privacy Impact Assessment: Not reported
Authorization to Operate: No
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

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

Pre-deployment testingReported in progressSource: b) In-progress

AI impact assessmentReported in progressSource: b) In-progress

Independent reviewReported in progressSource: d) In-progress

Ongoing monitoringReported in progressSource: b) Development of monitoring protocols is in-progress

Operator trainingReported in progressSource: b) Establishment of sufficient and periodic training is in-progress

Fail-safeReported in progressSource: c) In-progress

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationReported in progressSource: e) In-progress

Potential impacts and how they were identified

Consistent with Executive Orders and OMB guidance, the case owner relied on DOJ AI governance practices to evaluate impacts and risks.