Illicit Trade
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
- 2023-07-25 (day precision)Source: 2023-07-25T00:00:00
- Withheld from public reporting
- Not reported
Mission
- Topic area
- Law Enforcement
- Operational functions
- Forecasting and prediction Derived by keyword rules; see methodology
Problem the AI is intended to solve
The use case is designed to improve the identification and prioritization of high-risk inbound cargo shipments that may violate trade regulations. Using advanced AI and machine learning models, the system enhances risk assessment processes, helping CBP personnel more effectively detect suspicious shipments and potential compliance issues. By analyzing historical data, risk attributes, and employing predictive modeling, the AI supports CBP in streamlining enforcement actions and improving the accuracy of targeting shipments for additional review and screening. This approach helps optimize resource allocation and strengthens CBP's ability to enforce trade regulations efficiently.
Expected benefits
The model identifies high-risk shipments to support CBP personnel in managing their workload associated with detecting threats and selecting candidate shipments for review and additional screening.
System outputs
The model results are sent to the Automated Targeting System for review and assessment by operational personnel, who may conduct additional screening if necessary.
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.
Potential impacts and how they were identified
Risks include false positives and negatives, which could lead to unnecessary inspections or missed detections; and bias in algorithms that may disproportionally target certain importers. These risks have been identified through research, real-world applications, and expert analyses.