Agriculture Commodity Model (AGC)

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

The AI outputs do not produce an action or serve as a principal basis for a decision that has the potential to significantly impact the safety of human-life or well-being, climate or environment, critical infrastructure, or strategic assets or resources. The use case identifies agricultural pest risk in cargo shipments entering the United States. If a cargo shipment is identified as high-risk for pest infestation, it is prioritized for inspection. The model does not track or identify individuals.

Start date

2022-07-01 (day precision)Source: 2022-07-01T00:00:00

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

AI/ML Model aids CBP personnel in accurately and efficiently identifying cargo shipments at risk for agricultural pests in compliance with APTL's agricultural monitoring program. Due to the excessive volume and velocity of inbound cargo shipments, CBP personnel cannot possibly evaluate every shipment for risk. AI/ML models assist in performing a greater depth of risk assessment across all inbound cargo shipments to identify the most likely ones that would require additional attention, analysis and possible examination.

Expected benefits

The AGC Model uses data analytics and risk indicators to prioritize inspections and allocate resources effectively. This proactive approach helps protect the U.S. food supply and agricultural economy while facilitating legitimate trade.

System outputs

CBP agriculture specialists use the AGC Model's risk assessment outputs, such as risk scores to prioritize further screening of cargo shipments.

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

Not reported

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