2_use_case_name: ICE Mobile Check-in Application
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The Check-In App is an opt-in capability that allows for low-risk adult noncitizens to securely check-in via an ICE Verified user account, verify their identity, identify their location, and satisfy check-in requirements thereby reducing in-person office visits and reducing manual processing work. AI-enabled capabilities within the ICE Check-In application give ICE the ability to scale its interactions to a greater number of noncitizens and reduce in-office presence at ICE facilities.
12_outputs: AI/ML is used for this face detection and tracking. Once captured, the photo is verified with a 1:1 verification using the CBP Traveler Verification Service. Verification of all requirements will satisfy check-in requirements. A failed verification will result in a review by an officer. The officer can request the user try again or request the user use the default in-person check-in process by making an appointment with their local ICE office.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 7/10/2023
19_date_acq_dev_began: 10/2/2023
20_date_implemented: 11/30/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70CTD022D00000010
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
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 missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
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: ICE Portal
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: There is a risk that the face detection model testing will show the model performs poorly for certain performance metrics that cannot be remediated through software configuration or other reasonable means. In that scenario, ICE will either pursue using a different face detection model or pursue additional testing to uncover the root cause of poor performance.
55_independent_eval: Yes – by the CAIO
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: None for liveness detection using Google ML Kit. For facial verification, ICE leverages CBP TVS and inherits their controls.
63_stakeholder_consult: Direct user testing
65_appeal_process: Yes
67_opt_out: Yes