2_use_case_name: Traveler Identity Verification Services (Vetting)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: CBP's Traveler Identity Verification Services (Vetting) utilizes facial recognition technology to enhance threat identification by matching travelers' biometrics against records of concern. 
12_outputs: When the system identifies a potential match to concerning records, CBP personnel conducts a manual facial comparison to determine whether the record is likely associated with the individual.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 12/1/2017
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 12/1/2018
22_dev_method: Developed with contracting resources.
23_contract_piids: 024-000005265
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Border Crossing Information
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Traveler Verification Service
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: This AI system has been tested in operational or real-world environments and risks have been identified and mitigated. Information about risks and mitigations for this use case may be law enforcement sensitive; DHS continues to review details for potential future disclosure in accordance with applicable law and policy.
55_independent_eval: Yes – by the CAIO
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: In-person,Other
59_question_type: Facial Matching and privacy notices where FR tech is in use.
61_adverse_impact: No
62_disparity_mitigation: The threshold for the biometric matching was tested extensively with a variety of face types for several months to establish a match threshold for the identification.
63_stakeholder_consult: None of the above
65_appeal_process: Yes
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.