DHS · DHS-80 · record R1904

Traveler 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: High-impactSource: a) High-impact
Impact justification: Not reported
Start date: 2012-12-01 (day precision)Source: 2012-12-01T00:00:00
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

Traveler Entity Resolution AI/ML models aim to improve both security and operational efficiency by focusing on individuals who may present higher risks, by improving the certainty of traveler record matches to assist CBP personnel in identifying suspicious travelers for follow-on action.

Expected benefits

To enhance the efficiency and effectiveness of screening passengers for potential security risks. The AI model assess traveler data such as travel patterns and historical records assisting CBP personnel to prioritize higher-risk individuals for further screening, streamlining the vetting process, allowing CBP personnel to focus resources on the most high-risk travelers, thereby improving border security while reducing the burden of manual screening.

System outputs

The outputs are integrated into the Automated Targeting System (ATS), which generates notifications to recommend further inspection or follow-up actions. These recommendations assist CBP personnel in making real-time decisions about which travelers to prioritized for further screening. CBP personnel retain the final authority in the decision-making process, ensuring that human judgment remains central to border security operations.

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: b) Sex; c) Age
Training and evaluation data: This model leverages data housed within the Automated Targeting System (ATS) Unified Passenger (UPAX).
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.

Pre-deployment testingReported in placeSource: a) Yes

AI impact assessmentReported in placeSource: a) Yes

Independent reviewReported in placeSource: c) Yes – by the CAIO

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeReported in placeSource: a) Yes

Appeal processPrecluded by law or guidanceSource: d) Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal

User and public consultationOther or ambiguous answerSource: d) Other

Potential impacts and how they were identified

Risks include false positives and negatives, which could result in delays for travelers, failure to detect narcotics smuggling, or missed detection; algorithmic bias may disproportionately target certain types of travelers and crossing behaviors (related to model training using historical seizures); and ongoing challenge of traffickers adapting their methods to evade detection. These risks have been identified through research, real-world applications, and expert analyses.