GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Genomic Analyses of Pathogen Subtypes
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FSIS: Food Safety and Inspection Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this use case is to use machine learning (ML) methods to group foodborne germs based on patterns in their genes, then connect this information with available health data to evaluate foodborne germ risk to public health. Expected benefits include improving our understanding of important foodborne germ genes, assessing key genes and new trends, and identifying and ranking germs that are important for public health.
12_outputs: The model outputs predictions of high risk foodborne germ subtypes, key genetic markers by importance, and emerging trends.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2022
19_date_acq_dev_began: 2/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is composed of agency-owned data, including whole genome sequencing (WGS) data from Food Safety and Inspection Service (FSIS). Sampling programs are publicly available and hosted on the National Center for Biotechnology Information (NCBI) database.
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.
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: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
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.
2_use_case_name: Foodborne Illness Source Attribution
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FSIS: Food Safety and Inspection Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The Interagency Food Safety Analytics Collaboration (IFSAC) - a partnership between the Centers for Disease Control and Prevention (CDC), the U.S. Food and Drug Administration (FDA), and the Food Safety and Inspection Service (FSIS) - has used computer-based methods to predict the likely sources of foodborne illnesses in humans caused by various germs (e.g., Salmonella, Campylobacter). Expected benefits include improving our understanding of where these germs come from and how they spread, which can help in creating measures and policies to prevent or reduce illnesses and the overall impact of these diseases.
12_outputs: The model outputs predictions of likely sources of foodborne human illness cases, along with a confidence score of how probable it is that the illness came from the predicted source.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/2/2021
19_date_acq_dev_began: 2/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is composed of agency-owned data, including whole genome sequencing (WGS) data from Food Safety and Inspection Service (FSIS). Sampling programs are publicly available and hosted on the National Center for Biotechnology Information (NCBI) database.
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.
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: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
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.
2_use_case_name: Public Comments Analysis
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: APHIS: Animal and Plant Health Inspection Service; AMS: Agricultural Marketing Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of the model is to automate the analysis of comments from regulations.gov to help personnel in their review and response tasks. Benefits include a reduction in the number of labor hours needed for review and response.
12_outputs: The model outputs text analysis and categorization of the public comments. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 11/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
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.
40_has_ato: Yes
41_system_name: Marketing and Regulatory Programs Azure GSS; Marketplace for Data & Analytics (MDA)
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: In development
47_timely_resources: No
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
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.
2_use_case_name: Rangeland Analysis Platform
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: ARS: Agricultural Research Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: The Rangeland Analysis Platform (RAP) allows users to track changes in plant growth and coverage over time. By monitoring the condition of agricultural ecosystems and the impact of conservation efforts, it can guide conservation practices for wildlife habitats, carbon assessments, and tax assessments.
12_outputs: The system outputs estimated fractional plant cover and net primary productivity estimates.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2022
19_date_acq_dev_began: 3/1/2023
20_date_implemented: 1/1/2017
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is collected from over 75,000 monitoring locations collected by Natural Resources Conservation Service (NRCS), Bureau of Land Management, and other agencies. These data were combined and stored in the Landscape Data Commons (www.landscapedatacommons.org).
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: Google Earth Engine, Google Cloud
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
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.
2_use_case_name: Predictive Cropland Data Layer
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NASS: National Agricultural Statistics Service
8_topic_area: Other
8_question_type: Agricultural Statistics
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this system is to predict crop rotations. Benefits include improving data quality of area-based surveys.
12_outputs: The system outputs predictions of the types of crops in specific locations within the Conterminous United States (CONUS).
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
20_date_implemented: 2/15/2021
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is derived from Cropland Data Layer (CDL) and Farm Service Agency (FSA) administrative data. CDL is produced using satellite images and extensive agricultural ground reference data.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: 6-12 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: 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.
2_use_case_name: Dam Inspection Report Document Processing
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NRCS: Natural Resources Conservation Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this AI is to pull out and organize data from thousands of dam inspection documents so that we can use Microsoft Power BI to understand the condition of thousands of USDA Watershed program dams. This allows us to identify the biggest issues and trends across our collection of over 2,100 dams in Oklahoma while reducing labor hours required to complete the task manually.
12_outputs: The model outputs text and checkbox responses, including dam metadata, inspection issue tracking (yes and no checkboxes), and further remarks on the issue or what has been/needs to be done on the dam.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2023
19_date_acq_dev_began: 9/1/2023
20_date_implemented: 12/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is made up of dam inspection forms collected by dam owners (ex. conservation districts, cities) or Natural Resources Conservation Service (NRCS) staff. The data was validated through manual checks and is composed of 30+ dam inspection reports with different handwriting. The data contains 120+ variables that are read from each two-page inspection report. The data types are date, text, table (text variable types in the table), and checkbox binary outputs (checked: yes or no). The data is not publicly available.
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: AI Builder
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Worked with other NRCS leadership to get this model AI credits again for use once Microsoft AI Builder was reapproved.
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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.
2_use_case_name: Portfolio Approval and Management (PAM) Bot
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: ERS: Economic Research Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of the model is to improve the Economic Research Service (ERS) research approval process. The system reduces the time it takes to fill out information and seeking approval, improves information accuracy, and brings visibility to the approval status across various division functions.
12_outputs: The system provides three outputs: the approval status, summary recommendations, and generated citations.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
30_saop_review: No
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: GIS Invasive Tree Extraction for Field Level Users
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NRCS: Natural Resources Conservation Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: The puspose of the model is to estimate of the spread of invasive tree infestation, specifically Eastern redcedar. This helps to avoid poor or inaccurate estimates caused by time constraints and heavy workloads when manually collecting the data.
12_outputs: The model outputs polygons representing the extent of trees present in landscape.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/15/2019
19_date_acq_dev_began: 7/15/2019
20_date_implemented: 9/30/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is created from the National Agriculture Imagery Program (NAIP), aerial images of land taken from aircrafts. The data is not limited to NAIP, but the 2020 NAIP has proven to be the most reliable source data.
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: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: DISTRIB-II: Habitat Suitability of Eastern United States Tree
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose and expected benefits of the Climate Change Atlas are to give forest resource managers, forest landowners, and the general public information on the current and potential future of habitats for various tree species in the eastern United States. This information can contribute to forest management decisions when considering how climate change may affect the trees currently present and how likely it is that other tree species not currently in an area might find new habitats under different climate change scenarios.
12_outputs: The system outputs predictions of how well a tree species can live in a certain habitat based on climate change scenarios. Maps, graphs, and reports are generated from the modeled geographic information systems (GIS) data.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/10/1998
19_date_acq_dev_began: 5/16/2016
20_date_implemented: 9/12/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: The input data and predictions have been evaluated using standard statistical methods, reviewed by several experts and many users, and the project has been published in several peer-reviewed journals.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is created from publicly available Forest Inventory and Analysis data.
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: 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: 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.
2_use_case_name: FSA FLP Chatbot
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FSA: Farm Service Agency
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this use case is to solve the problem of searching Loan handbooks to provide better customer service. The expected benefit is to help employees provide better service. A second benefit that is being explored is providing Veteran specific answers to services.
12_outputs: The expected output is text answers to prompt questions. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 12/14/2023
19_date_acq_dev_began: 3/29/2024
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is collected from the 1 and 3 Farm Loan handbooks that are available on USDA intranet. The handbooks contain text, tables, and charts. There are also images such as screen shots in the handbooks.
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.
35_demo_features: Other
35_question_type: Veteran
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Other
43_question_type: Using DISC Incubator
45_compute_request: No
47_timely_resources: No
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: ROE Document Recognition - RoeDR
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: RMA: Risk Management Agency
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this model is to analyze documents from producers and Authorized Insurance Providers (AIPs), pick the appropriate page from the documents, read the signature date and producer signature name, convert the date and name to text, and load it into an application. This feature saves us time from having to input the data manually.  We can then use the data for reporting purposes.
12_outputs: The model outputs the producer signature and signature date within document as text.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/15/2024
19_date_acq_dev_began: 5/23/2024
20_date_implemented: 6/12/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is created from actuarial change forms and determined yield requests received from authorized insurance providers. The data contained 150 documents. 
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
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: No
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
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.