Babel
Description
Babel utilizes AI modules for text detection and translation as well as object and image recognition to provide analysts with possible matches to manually review in a single interface, versus doing multiple manual queries. The output is not singly used for action or decision making.
Detailed example
Babel utilizes AI modules for Text detection and translation as well as object and image recognition to provide analysts with possible matches to manually review in a single interface versus doing multiple manual queries. The output is not singly used for action or decision making and are used to identify additional Open Source or Social Media of a person or identify additional selectors (such as phone and emails) that are previously unknown to CBP and compared by an analyst against Government systems to identify additional derogatory information. These factors can often eliminate the traveler from additional screening.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
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
CBP uses this tool to conduct targeted queries to aid CBP in open source research to monitor potential threats or dangers or identify travelers who may be subject to further inspection for violation of laws CBP is authorized to enforce or administer.
Controls / human review
ATO: Yes; PIA: https://www.dhs.gov/publication/dhscbppia-058-publicly-available-social-media-monitoring-and-situational-awareness
Data needed
Babel uses proprietary data, public datasets, and machine-labeled datasets to train its NLP and matching models. Evaluation data includes human-annotated datasets, precision, recall, and F1 score assessments, and customer-provided labeled name pairs for tuning. All returned results are carefully reviewed, and no sensitive CBP-owned data is involved in the process