GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Landscape Change Monitoring System (LCMS)
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: This project monitors large areas for changes in land cover and land use over time. The benefits include creating a consistent method for tracking changes in the landscape.
12_outputs: The model outputs predictions of vegetation gain, vegetation loss, land cover, and land uses.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/15/2016
19_date_acq_dev_began: 1/1/2017
20_date_implemented: 3/1/2021
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Providing International Organization for Standardization metadata and independent map validation results.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Training and evaluation data used includes 15,000 sample plots spanning from 1985 - 2019. Features of the dataset are land cover, land use, and change process categories. Data is available upon request.
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: Yes
41_system_name: Google Cloud Platform contract
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 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 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: Forest Health Detection Monitoring
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This project monitors forest health by detecting tree damage through changes in light patterns collected by satellites. This detection method helps the Forest Health Protection program monitor areas that can't be checked on the ground or with aerial surveys.
12_outputs: The model outputs the stage of forest health based on the image, along with a map (polygons) of the area for monitoring.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/29/2021
19_date_acq_dev_began: 11/18/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1284JC18D005/1284JC20F0036
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Training data was collected from the National Agriculture Imagery Program (NAIP) and WorldView imagery. Similar high resolution imagery has been used in evaluation and validation. There are approximately 1000 labeled images in the training dataset, each containing spectral data. The data are not publicly available.
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: Yes
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: 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: This project produces supplemental estimates of crop acreage and releases geospatial data products to the user community.
12_outputs: The system outputs are an acreage estimate and agrigulture-specific land cover product.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2008
19_date_acq_dev_began: 1/1/2008
20_date_implemented: 1/1/2008
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: USDA/Farm Service Agency Common Land Unit data, consisting of Geographic Information System (GIS) shapefiles and associated attribute data, was used for training and testing.  
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: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: See5
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: List Frame Deadwood Identification
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: This model helps identify farms that may be out of business on the National Agricultural Statistics Service list. Parts of the model were used to create clear rules to identify these farms. The resulting list is more accurate and allows for smaller sample sizes, reducing the burden on respondents.
12_outputs: The output of the model was a probability score that a farm is out of business. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/4/2014
19_date_acq_dev_began: 2/3/2016
20_date_implemented: 1/24/2018
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: Yes
33_agency_data: National Agricultural Statistics Service List frame and survey data, consisting of features put together to form profiles of farming operations, was used to train and validate the model.
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: Age
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: Research, Education, and Economics (REE) - National Agricultural Statistics Service (NASS) - Citrix Environment
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
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: 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: Climate Change Classification NLP
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NIFA: National Institute of Food and Agriculture
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Climate Change Classification Natural Language Processing (NLP) model identifies likely climate-related projects within National Institute of Food and Agriculture's (NIFA) large and diverse funding portfolio. Expected benefits include reduced labor hours for reporting and increased repeatability and accuracy of reporting.
12_outputs: Model output is a list of climate change projects classified as "climate change related" or "not climate change related" for National Institute of Food and Agriculture (NIFA) internal project review/adjudication and reporting.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2021
19_date_acq_dev_began: 7/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: HHSN316201200002W
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: This model is trained on publicly available project information provided by the project directors via National Institute of Food and Agriculture's (NIFA) REEport reporting portal.  This includes text fields containing the project's title, summary, objectives, and keywords.  The training data is catagorized by the project team. 
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: More than 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.
2_use_case_name: Video Surveillance System
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: OSSP: Office of Safety, Security, and Protection
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this system is to conduct facial recognition video surveillance to provide enhanced security. Benefits include reduced labor hours for technicians and augmented surveillance capability.    
12_outputs: The system outputs a positive match to the security control center, indicating identification of the selected individual. An alarm notification is sent to alert security personnel.    
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 2/14/2020
19_date_acq_dev_began: 2/14/2020
20_date_implemented: 2/14/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 12314420A0001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Sex/Gender, Race/Ethnicity
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: 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: No
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: The key risk of this system is the misidentification of an individual.
55_independent_eval: Planned or in-progress
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: Yes
63_stakeholder_consult: None of the above
65_appeal_process: Yes
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.
2_use_case_name: Aquisition Approval Request Compliance Tool
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: OCIO: Office of the Chief Information Officer
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This project was developed to help identify likely Information Technology (IT) purchases that do not have an associated Acquisition Approval Request. The benefits are reducing unauthorized IT purchases and increasing compliance with IT procurement procedures and approvals.
12_outputs: The output is a score indicating how likely it is that the purchase is an Information Technology (IT) purchase.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/31/2019
19_date_acq_dev_began: 4/6/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 12314419D0006
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The model was developed using text entries entered into USDA's Integrated Acquisition System (IAS).
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.
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: 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: Operational Water Supply Forecasting for Western US Rivers
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 National Water and Climate Center has a multi-model machine-learning metasystem (M4) for generating water supply forecasts. This model uses AI and other data-science technologies to reduce forecast errors, helping stakeholders make better decisions about water supply availability.
12_outputs: The model outputs water supply forecasts.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/1/2019
19_date_acq_dev_began: 12/1/2019
20_date_implemented: 1/1/2024
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Natural Resources Conservation Service - USDA
26_public_service: Applying for the Environmental Quality Incentives Program (EQIP)
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data contains snow and precipitation metrics from the Natural Resources Conservation Service (NRCS) Snow Survey and Water Supply Forecast program Snow Telemetry (SNOTEL) monitoring network. 
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: 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: 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: Standardization of Cut Flower Business Names for Message Set Data
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: APHIS: Animal and Plant Health Inspection Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Natural Language Processing (NLP) is used to match names of producers to varietal information for cut flowers, which will help convert from manual to automated inspection systems. The main benefit of automation is that it can manage thousands of entities, which would be impossible to handle manually.
12_outputs: The model outputs before and after lists of producer names and cut flower varieties.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 1/1/2024
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: Intelligent Ticket Routing
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: OCIO: Office of the Chief Information Officer
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Help desk tickets are often sent to the wrong group and must be manually re-routed to the correct group, which can take time, resources, and may delay issue resolution. The Intelligent Ticket Routing system helps to send the ticket to the correct group, increasing customer satisfaction by reducing the number of times a customer is transferred or placed on hold, and decreasing the average handle time (AHT). In our specific use case, we reduce the time taken to route a ticket to the appropriate group, shortening the time required to resolve an issue.
12_outputs: The system outputs a prediction of the appropriate group for ticket management.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 1/1/2022
20_date_implemented: 1/1/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: ITSS Contract
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 a collection of scripts from Remedy reporting database, a structured database containing freeform text fields. More than 100,000 samples and 526 features comprise the data, including numerical, categorical, text, and date types. Data is not public.
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: Yes
41_system_name: Midrange + Moving to Sagemaker (ATO in progress)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: DISC / Hybrid Hosting Infrastructure
45_compute_request: Other
45_question_type: DISC / ERMS Portal - SD105 Request, must be DISC employee, or customer.
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.