2_use_case_name: PreCheck Touchless Identity Solution (TIS)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: TSA is using Facial Comparison to verify a passenger’s identity at its security checkpoints and CMAP locations using the CBP Traveler Verification Service (TVS). This process streamlines passenger and crewmember identity verification, increasing the speed of security checks while maintaining a high degree of safety for all passengers and crewmembers."
12_outputs: TSA is leveraging CBP's TVS system technology as an optional process for passengers or crew members traveling via certain airports who wish to further expedite their TSA PreCheck or crew member ID verification process. This additional TSA PreCheck feature is voluntary, and passengers may opt-out of the process at any time and instead choose the standard identity verification by a Transportation Security Officer (TSO). Crew members that wish to opt-out will be sent to the security checkpoint to process through screening.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 4/1/2016
19_date_acq_dev_began: 4/1/2017
20_date_implemented: 10/1/2018
22_dev_method: Developed with contracting resources.
23_contract_piids: 024-000005265
24_hisp_support: Yes
25_hisp_name: Transportation Security Administration – DHS 
26_public_service: Completing TSA passenger screening
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Data repository owned by another agency.
33_agency_data: N/A - The Agency does not own any of the data used to train, fine-tune, and/or evaluate performance of the model(s) used in this use case.
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: CBP TVS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Provisioned through CBP.
45_compute_request: Other
45_question_type: CBP has process in place to request access
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: Impact evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution and has utilized randomized experiments with a control group or other counterfactual, social systems analysis, or other rigorous research methodologies to evaluate impact and identify potential harm to users as well as broader groups of people.
54_key_risks: The key risk is the degradation of the TVS verification to degrade overtime based on the parameters of assessment for comparing images to templates. This was mitigated by testing the threshold for the biometric matching extensively with a variety of face types for several months to establish a match threshold for the identification. The algorithms have been trained on hundreds of millions of data points from a combination of public and private data sources. The underlying data covers a wide variety of demographics (e.g., age, race, gender, etc.) representative of the general population. During model training and refinement shortcomings (e.g., certain combinations of flesh tones and lighting, facial hair variations, etc.) were compensated for. The algorithms are industry leading as measured by NIST. This mitigates the risk of poor performance and algorithmic discrimination. 
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: Online - in the terms or instructions for the service.,In-person,Other
59_question_type: Facial matching and privacy notices are posted where facial recognition technology 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: Direct user testing,General solicitations of comments from the public
65_appeal_process: Yes
67_opt_out: Yes