Traveler Entity Resolution
Description
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.
Detailed example
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.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
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.
Controls / human review
ATO: Yes; PIA: https://www.dhs.gov/sites/default/files/publications/privacy_pia_cbp_tsacop_09162014.pdf
Data needed
This model leverages data housed within the Automated Targeting System (ATS) Unified Passenger (UPAX).