DHS · DHS-24 · record R1927

Entity Resolution

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: 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. AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services.
Start date: 2023-05-01 (day precision)Source: 2023-05-01T00:00:00
Withheld from public reporting: Not reported

Mission

Topic area: Law Enforcement
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The use case leverages advanced technology to improve the analysis of global trade data and enhance its ability to identify risks within supply chains. By using AI tools to organize and analyze complex datasets, CBP can uncover patterns and relationships that may indicate unethical practices, such as forced labor. This innovative approach supports efforts to ensure compliance with trade laws, protect economic security, and promote fair and ethical trade practices.

Expected benefits

Detection research of force labor within supply chain utilizing analytical AI platform.

System outputs

Detection of potential force labor within supply chain.

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): Not reported
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): Altana
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: No
Privacy Impact Assessment: www.dhs.gov ↗
Authorization to Operate: No
Demographic features: Not reported
Training and evaluation data: Altana utilizes a combination of commercial, public, and proprietary data sources to build a searchable and traversable graph of global trade. These include bills of lading, customs declarations, and exclusive proprietary documentation from first-party logistics providers.
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.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported