DHS · DHS-2390 · record R1889

Cargo Security Assessment Model

Department of Homeland Security · CBP

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: 2011-12-01 (day precision)Source: 2011-12-01T00:00:00
Withheld from public reporting: Not reported

Mission

Topic area: Law Enforcement
Operational functions: Risk scoring and triage Derived by keyword rules; see methodology

Problem the AI is intended to solve

This use case addresses the challenge of efficiently identifying and mitigating risks associated with cargo shipments entering the United States. With the high volume of shipments processed daily at ports of entry, it is essential to detect potentially high-risk shipments, such as those that may pose security threats, without causing delays to legitimate trade and commerce. This use case uses advanced data analytics and machine learning to enhance the ability to evaluate and prioritize shipments for further review, ensuring that flagged cargo is inspected appropriately while maintaining efficient cargo processing operations.

Expected benefits

AI/ML Models identify high risk shipments to aid CBP officers in detecting narcotics smuggling threats, identifying candidate shipments for review and referral for inspection at CBP Ports of Entry (POEs).

System outputs

High risk model results are returned to users as a system rule hit. These rule hits are viewable in the associated system results window. From this window, CBP operational personnel review and assess result for next action, including possible shipment examination.

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): Automated Targeting System (ATS)
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
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: www.dhs.gov ↗
Authorization to Operate: Yes
Demographic features: Not reported
Training and evaluation data: This model leverages data provided by carriers within the Automated Commercial Environment (ACE), as well as transformations of that data within the Automated Targeting System (ATS).
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 placeSource: a) Yes

AI impact assessmentReported in placeSource: a) Yes

Independent reviewReported in placeSource: c) Yes – by the CAIO

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeReported in placeSource: a) Yes

Appeal processPrecluded by law or guidanceSource: d) Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal

User and public consultationOther or ambiguous answerSource: d) Other

Potential impacts and how they were identified

Risks include false positives and negatives, which could lead to unnecessary inspections or missed detections; bias in algorithms that may disproportionately target certain importers; and ongoing challenge of traffickers adapting their methods to evade detection. These risks have been identified through research, real-world applications, and expert analyses.