2_use_case_name: Individual Assistance (IA) & Public Assistance (PA) Projections
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of these supervised learning models is two-fold:

1.	For informational purposes: the models will produce predictions for to-be-determined quantities of interest. These quantities are often of interest to Agency personnel in the field, region, and headquarters, as well as DHS, OMB, NSC, and the Whitehouse.
2.	For decisional purposes: in addition to being informative, the model’s predictions are likely to be used for decision making. This may include implicit use (e.g., seeing that there are a large number of households that require assistance may motivate FEMA CFO to consider requesting additional funding for the Disaster Relief Fund) or explicit use (e.g., the number of estimated housing units required may be used to determine the number of housing units to order just-in-time).

Anticipated benefits of using supervised learning models include:
1.	Increased predictive performance: supervised learning models offer means of quantifying and choosing models with the best possible predictive performance.
2.	Timely prediction: supervised learning models can produce reasonably high performance predictions based on limited data.
3.	Quantification of uncertainty: supervised learning models are able to provide prediction intervals and predictive distributions in addition to point predictions to give users a sense of how uncertainty is inherent in the prediction.
4.	Rigor: supervised learning models are well founded on statistical learning theory and provide a level of mathematical rigor that speaks to the reliability and limitations of methods.
12_outputs: These supervised learning models produce predictions, not recommendations and not decisions (though they can be used to inform human users in making recommendations and decisions).

At minimum, these supervised learning models will produce point predictions for the different quantities of interest for disaster declarations. Additionally supervised learning models may produce prediction intervals or predictive distributions as feasible and appropriate for the given prediction problem. Often these outputs will be shared via business intelligence tools (e.g., Tableau or PowerBI) for wide internal FEMA use. Some predictions may be shared to a more restricted audience through simpler means (e.g., an excel workbook) as appropriate.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2018
19_date_acq_dev_began: 8/1/2018
20_date_implemented: 6/1/2024
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Federal Emergency Management Agency – DHS 
26_public_service: Applying for and receiving disaster assistance
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Data from the National Emergency Management Information System (NEMIS) and Grants Manager
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: Less than 6 months
43_infra_provisioned: No
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.