GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Predictive Maintenance Impacts
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: A natural language processing (NLP) model classifies whether infrastructure maintenance changes will or will not cause an incident at the Digital Infrastructure Services Center (DISC). Using this system, the business can improve the review process or address specific needs within groups. This will lead to process improvements, increased productivity, higher performance and job satisfaction, higher client satisfaction, and better achievement of key performance indicators (KPIs).
12_outputs: The model outputs a score between 0 to 1. Closer to 1 indicates higher likelihood of an incident created by the proposed change.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2020
19_date_acq_dev_began: 3/1/2020
20_date_implemented: 3/1/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: ITSS Contracts
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The dataset is composed of Remedy data, containing inventory data of all of assets at DISC: hardware, software, incident tickets, and change tickets. Data contains numerical, categorical, text, and date data types.
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 (current) + Sagemaker (ATO in process now - future)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: DISC / ERMS Portal - SD105 Request, must be DISC employee, or customer.
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.
2_use_case_name: Artificial Intelligence SPAM Mitigation Project
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: OASCR: Office of the Assistant Secretary for Civil Rights
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: An AI/ML model automatically classifies and removes spam and marketing emails from civil rights complaints email channels. Benefits include reducing the time spent manually managing email channels, decreasing the memory burden on email systems, and lowering the risk from malicious emails. 
12_outputs: The model outputs a classification of received emails, flagging spam, marketing, and phishing emails.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/30/2023
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: Approximate String Matching (aka fuzzy matching) to Standardize 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: A model is used to replace typos in Plant Protection and Quarantine (PPQ) program data using a list of standardized producer and commodity names. This results in clean, standardized data through an automated workflow. Benefits include reducing labor hours compared to manual data cleaning, makes near-real-time reporting possible, and accurate data enables program managers to conduct efficient policy enforcement and program monitoring.
12_outputs: The model outputs corrected text data.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2023
19_date_acq_dev_began: 2/23/2023
20_date_implemented: 5/16/2023
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 used includes data tables from the Agriculture Risk Management (ARM) data system, Agricultural Commodity Import Requirements (ACIR), and the Participating Government Agencies (PGA) Message Set. The cleaned data is displayed in a dashboard for program monitoring. Quality control data are exported from each matching routine and used to validate results as needed.
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: 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: 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: Automated PDF Document Processing and Information Extraction
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: This use case takes program and workforce related information stored in thousands of PDFs and converts the information into data tables that can be used for analytics and dashboards. This makes information that is difficult to find available in real-time to support decision making and saves large amounts of time compared to previous methods used.
12_outputs: The model outputs structured database tables.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/15/2022
19_date_acq_dev_began: 10/24/2022
20_date_implemented: 1/15/2023
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 used is from Agriculture Vessel Inspection forms concerning flighted spongy moth complex, sent from Customs and Border Protection to Plant Protection and Quarantine.
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Microsoft Power Automate
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: Census Propensity Scores via ML
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 predicts how likely individuals or operations are to complete the Census of Agriculture. The predictions can help data collectors decide where they need to focus their efforts in order to get more complete census responses.
12_outputs: The model outputs a probability score (all values from and including 0 to 1).
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 10/1/2022
20_date_implemented: 11/1/2022
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 collected from the 2017 Census of Agriculture, which includes number of farms by size and type, inventory and values for crops and livestock, producer characteristics, and much more.
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: Other
35_question_type: Zipcode
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: 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: Ecological Site Descriptions (Machine Learning)
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: This AI/ML work conducts analysis of over 20 million records of soils data and 20,000 text documents of ecological information in order to provide complete soil based ecological information for the country. Benefits include reduction in labor hours manually analyzing documents, and enabling stakeholders to examine records in ways previously not thought of to make more informed decisions.
12_outputs: The AI model outputs ecological soil classifications and mappings.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/30/2023
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: Conservation Effects Assessment Project
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 the use case is to predict the conservation effects of cropland practices in real time, with no technical skill required. Such models would allow field conservation planners to have real-time conservation effects on sediment and nutrients.
12_outputs: The model outputs predictions of sediment and nutrient change values based on conservation methods.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2021
19_date_acq_dev_began: 11/1/2021
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 Conservation Effects Assessment Project Survey cropland assessment, which measures trends in cropland conservation practices and their outcomes over time.
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: R Studio
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.
2_use_case_name: Digital Imagery (no-change) for NRI Program
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: AI algorithms are used to look at landscape images and detect if the landscape has changed from year to year. Currently, about 72000 aerial images are interpreted by dozens of technicians each year to collect data for the National Resources Inventory (NRI) program. This case would decrease the number of labor hours required of technicians to manually interpret images.
12_outputs: The model outputs a classification of “no-changes” in the images if the landscape in the images remain stable from year to year.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2022
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: Nutrition Education & Local Access Dashboard
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FNS: Food and Nutrition Service
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The goal of this dashboard is to provide county-level information on nutrition education and local food access, alongside other metrics related to hunger and nutritional health. This interactive dashboard can provide specific details based on the properties of farm to school intensity and size, program activity intensity, ethnicity and race, fresh food access, school size, and program participation. These properties allow users to find similar states based on any of these characteristics, opening up opportunities for partnerships with states they may not have considered. Benefits include increasing stakeholder awareness and empowering more informed decision-making and collaboration.
12_outputs: The model outputs groups of similar counties/states based on the different combinations of properties available for states. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/9/2022
19_date_acq_dev_began: 1/20/2023
20_date_implemented: 6/23/2023
22_dev_method: Developed with both contracting and in-house resources.
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 composed of numerical and categorical data describing farm to school intensity and size, program activity intensity, ethnicity and race, fresh food access, school size, and program participation.
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.
35_demo_features: Race/Ethnicity
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: 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: Survey Text Remarks Value Scoring
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NASS: National Agricultural Statistics Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this use case is to analyze a large amout of text in survey responses and score all comments with a priority value. The highly scored blocks of text then get prioritized for review by a human and are responded to more quickly than if they were to be retained in a queue.  
12_outputs: The model outputs a value score on each snippit of text, highly scored snippits of text are placed at the front of the queue before lower scored blocks to capture text of value more quickly.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/24/2021
19_date_acq_dev_began: 12/27/2021
20_date_implemented: 4/20/2022
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: The training data is created from historically collected text and survey remarks. 
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: Yes
41_system_name: ELMO
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: This is not needed
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: Survey Outlier Detection Model
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 use case is to identify abnormal values to edit in surveys. This reduces manual labor and improves data quality.
12_outputs: The model outputs a recommendation of which values in a dataset should be changed.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2022
19_date_acq_dev_began: 5/1/2022
20_date_implemented: 5/27/2022
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 collected from National Agricultural Statistics Service Survey Data, which is structured data containing categorical and numerical values.
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 – source code is publicly available.
39_code_link: https://jds-online.org/journal/JDS/article/1383/file/13236
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: Multilingual Translation of Recalls and Public Health Alerts
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FSIS: Food Safety and Inspection Service
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this system is to expand the multilingual outreach of food safety information like recalls and public health alerts. Benefits include cost savings on vendor translation services, faster messaging circulation, and increased number of available languages to the general public.
12_outputs: The model outputs multilingual translations created from the original english text.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/15/2023
19_date_acq_dev_began: 6/15/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Leveraging Azure's COTS product service
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
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. 
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.