Entity Resolution for Global Trade Data

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

Although the use case falls into a presumed high-impact category related to law enforcement investigations, it does not meet the high-impact definition as its outputs do not directly serve as a principal basis for enforcement actions or regulatory decisions. Instead, the AI-generated knowledge graph provides a foundation for further human-led investigation, requiring source validation through separate processes. This delineation ensures that the AI output remains a supportive rather than a determinative factor, disqualifying it from the high-impact AI system category.

Start date

2024-10-10 (day precision)Source: 2024-10-10T00:00:00

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 AI is intended to solve the problem of investigators having to manually piece together fragmented global trade and supply chain data from many sources, which makes it difficult to see relationships among entities and identify potential leads in transnational criminal investigations.

Expected benefits

This platform improves Homeland Security Investigations' ability to validate existing information, understand complex supply chain networks, and to generate leads in transnational criminal investigations.

System outputs

The platform uses AI Machine Learning (ML) models for data collection, data structuring, entity resolution, network analysis, and risk assessment. These ML processes contribute to the platform’s output, a dynamic knowledge graph and user-friendly interface for global supply chain research.

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

Purchased from vendorSource: a) Purchased from a vendor

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

Not reported

Authorization to Operate

No

Demographic features

Not reported

Training and evaluation data

The platform’s machine learning models were trained and evaluated by the vendor using its own datasets, which are derived from public and commercially sourced trade and logistics records (such as customs declarations, bills of lading, and shipment data from air, rail, and sea carriers). Homeland Security Investigations (HSI) does not provided any ICE or HSI investigatory data to the vendor to develop, training, test, or operate the platform models.

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