Cargo Classification Tool

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

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2020-10-01 (day precision)Source: 2020-10-01T00:00:00

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

The need to improve data classification, enable better machine learning integration, and facilitate nuanced trade entity risk assessments. By categorizing goods based on their descriptions and characteristics, these classifiers help identify potential threats associated with specific cargo types associated with prior violations.

Expected benefits

CBP’s Cargo Classification Tool improves trade compliance and enhances cargo risk assessment by streamlining the classification of goods, enabling better integration with machine learning systems, and refining entity risk evaluations. It identifies potential threats linked to specific cargo types and prior violations by categorizing goods based on their descriptions and attributes. These improvements contribute to faster, more accurate classification and risk-based targeting, which strengthens security and facilitates trade.

System outputs

The Cargo Classification Tool produces outputs that map cargo commodity descriptions to their most probable tariff codes, enhancing classification accuracy. These outputs integrate seamlessly into broader threat-specific risk models, providing features to support predictive risk assessments in cargo security.

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

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