Public Information Compilation for Travel Threat Analysis (Dataminr)
Description
Provides situational awareness of open-source social media and news reporting to enhance CBP Screening, Vetting and security of the homeland.
Detailed example
The AI output is compiled publicly available information for awareness. CBP employees further research the information, including reading the source information, to determine if there is a possible threat.
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
This tool significantly reduces the amount of time it takes for users to collect and compile commercially available open-source information when attempting to identify possible threats related to national security, border violence, CBP facilities, CBP employee safety and other topics with a CBP-nexus involving air, sea, and land travel to and/or from the U.S.
Audit / financial statement impact
The AI outputs do not produce an action or serve as a principal basis for a decision that has the potential to significantly impact the safety of human-life or well-being, climate or environment, critical infrastructure, or strategic assets or resources. The AI output only provides the officer with complied public information. The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services.
Controls / human review
ATO: Yes; PIA: https://www.dhs.gov/publication/dhscbppia-058-publicly-available-social-media-monitoring-and-situational-awareness
Data needed
Training data was collected from several publicly available, social media, and media outlet sites. This approach ensured the model was trained across several different groups representing an array of possible language types and vernaculars so as not to cause bias toward a specific demographic. Along with the above open-source data, the vendor leverages a mix of proprietary data to ensure the data is representative of real-world conditions and context.