2_use_case_name: Hurricane Score
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 Immigration and Customs Enforcement (ICE) Enforcement and Removal Operations (ERO) division would like to explore technology that could help lead to time savings and eliminate human error due to overburdened officers.  An AI generated hurricane score could also minimize the risk that a factor is overlooked due to officers being overburdened.  The hurricane score may be one factor that is used to inform the decision-making process. The “Hurricane Score” models the potential risk (1-5) that a noncitizen who is released from detention with the requirement to check in with ICE through monitoring will abscond from the program, with a higher number indicating a higher risk of absconding.
12_outputs: The Hurricane score model determines a probability of absconding and outputs a number (1-5), with a higher number indicating a higher risk of absconding. This number is used as one of many factors an officer uses when reviewing an ATD-ISAP case to determine the appropriate level of case management and technology for a noncitizen.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 2/1/2017
19_date_acq_dev_began: 2/1/2017
20_date_implemented: 2/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: Inactive case data from individuals enrolled in the ATD-ISAP program.
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. 
35_demo_features: Sex/Gender,Age,Other
35_question_type: Country of Origin
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: 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: 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: 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: There is a risk that the Absconder Model may include false positives and model bias. A false positive would occur when non-absconders are incorrectly classified with a score indicating a high probability of absconding. Model bias can arise if the training data is not representative of the full range of participants, leading to poor performance on certain patterns or characteristics. These risks are identified and if necessary remediated through error analysis during model testing and model performance monitoring. 
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: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: It is reevaluated and retrained on an annual basis.
63_stakeholder_consult: Direct user testing
65_appeal_process: Yes
67_opt_out: Other
67_question_type: H-Score adds one piece of info to a larger profile for decision-making by a human.