2_use_case_name: Low Probability of False Alarm (Low-Pfa) Algorithm for on-person screening.
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: The purpose is to reduce alarm rates while providing gender neutral detection which would increase passenger throughput and experience.

Utilizes Machine Learning (ML) to improve detection performance while decreasing alarm rates and passengers touch rates. The algorithm is gender agnostic which no longer requires officers to select a passengers gender prior to being scanned. AIT throughput and AIT utilization have increased with this new algorithm. Note: Once the algorithm is trained, it is locked down and no longer learning.
12_outputs: The AI outputs target coordinates to the operator viewing station which is viewed as a bounding box on a representative human figure.
16_dev_stage: Implementation and Assessment
17_impact_type: Both
18_date_initiated: 9/1/2021
19_date_acq_dev_began: 9/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: Contract: HSTS0417DCT1087 Task Order: 70T04022F7672N011
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: No
30_saop_review: No
31_data_catalog: No
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 has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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 published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
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: Security risks include false negatives allowing threats to get through the sterile side of the airport or high false alarm rates slowing operations. The program follows the Test & Evaluation (T&E) process documented in the TSA T&E Policy, T&E Guidebook, TSA Acquisition Qualification Policy, and TSA Third Party Testing Strategy, which includes independent test & evaluation. Various types of testing were conducted, including functional testing, developmental test & evaluation, independent test & evaluation, and integration/implementation testing. Tests were conducted on a variety of body types with varying concealment locations, concealment methods, and target types. Metrics include probability of detection, probability of false alarm, mean time between failure, mean down time, mean time to repair, and percentage of eligible passengers unable to be screened. All tests met or exceeded expectations. TSA performs regular checks to verify performance of the scanner (which includes the AI model) using Operational Test Kits (OTK). OTKs are used daily, whenever a scanner is moved, whenever a scanner is power-cycled, and as needed to verify performance. OTKs are created by each OEM and are specific to their scanners. They contain items that when concealed on a scanned person will result in a positive scan for a target. 
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.
61_adverse_impact: No
62_disparity_mitigation: The program follows the Test & Evaluation process documented in the TSA T&E Policy, T&E Guidebook, TSA Acquisition Qualification Policy, and TSA Third Party Testing Strategy, which includes independent test & evaluation.
63_stakeholder_consult: Direct user testing,General solicitations of comments from the public
65_appeal_process: Yes
67_opt_out: Yes