Verification Match Model
Description
By consolidating these into a single, unified Verification Match Model within a separate microservice, the use case aims to improve the accuracy of responses and reduce the need for manual review. ML plays a key role in the continuous improvement of these models, ultimately reducing the need for manual case reviews.
Detailed example
A recommendation and score that indicates person-and-record match probability used by verification systems (E-verify and SAVE) to improve accuracy in initial system response
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
Leveraging AI in the USCIS verification matching process of known records across systems is beneficial because it streamlines existing USCIS review by 1) improving associated system accuracy, 2) reducing human-error by automating person-and-record match scoring, and 3) matching at a higher volume than traditional tools or manual processes can capably achieve.
Controls / human review
ATO: Yes; PIA: https://www.dhs.gov/publication/dhsuscispia-030f-e-verify-mobile-app-usability-testing, https://www.dhs.gov/publication/systematic-alien-verification-entitlements-save-program
Data needed
Individual's Names, Dates of Birth, and Document Identifiers from USCIS sourced data contained in CIS2, C3, ELIS, and Global. These are all private datasets within USCIS.