GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Current Population Survey (CPS) Name Screening Tool
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case processes and classifies entries in survey data collect to identify if the name provided is a valid entry. The goal is to support and enhance processing and future data collection efforts to efficiently and accurately identify responses with invalid names.
12_outputs: Classifications for name entries.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/10/2023
19_date_acq_dev_began: 3/29/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Brite Group
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Sample of survey response data from the Current Population Survey (CPS)
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.
2_use_case_name: Linkage and Matching Program (LaMP)
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This use case involves probabilistic record linkage to link multiple person-level datasets. The predicted links are used for downstream processing to enable research regarding economic, demographic, and other topics and improve quality and efficiency of multiple Census operations.
12_outputs: The AI produces a prediction regarding whether two records match. Output from the model is pairs of records above a minimum threshold indicating a match along with the score describing the similarity between the two records in the pair.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/31/2023
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Brite Group, Harmonia
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Survey response data from decennial census operations.
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.
35_demo_features: Age
Sex/Gender
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 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.
2_use_case_name: Census Research Exploration and Analysis Tool (CREAT)
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: The Census Research Exploration and Analysis Tool (CREAT) is an experimental data tool from the Center for Economic Studies (CES) at the US Census Bureau that uses natural language processing to analyze, categorize, and sort the economic research contained in the CES working paper series. The goal of this project is to help CES researchers, managers, and other internal stakeholders explore connections among existing research, form new collaborations, and separate research into discrete topics. Working papers are sortable by author, tag, and keyword. Natural language processing (NLP) techniques are used to extract tags (using spaCy) and keywords (using KeyBERT) from the paper's text, as well as to determine working paper similarity (using Doc2Vec). This NLP usage highlights the value of these tools and techniques for making connections across economic research publications.
12_outputs: keywords, tags, and recommendations based on similarity.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 12/1/2023
20_date_implemented: 3/1/2024
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: No
33_agency_data: CES working paper metadata
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: 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.
2_use_case_name: Dr. NAICS LLM
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The core problem the Dr. NAICS LLM addresses is the ability to accurately and consistently place a NAICS code for a given business description. Because NAICS is such a large classification system with a lot of nuance it is sometimes difficult for businesses to identify the correct business description within it. The AI makes suggestions to improve that classification decision when reading incoming emails and voicemails from the general public.
12_outputs: The output is a set of recommendations for which NAICS codes best align with a text description of a business activity. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/27/2023
19_date_acq_dev_began: 11/27/2023
20_date_implemented: 9/30/2025
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1333LC23C00000030 and 1333LC23C00000026
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
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.
35_demo_features: Other
35_question_type: Does not use any demographic variables.
37_custom_code: No
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
45_compute_request: No
45_question_type: In the process of migrating the Dr. NAICS LLM beta app from the Reveal Server to inside the Census AWS server environment.
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: 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.
2_use_case_name: Statistical package syntax development and debugging
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: I. Interaction Terms Generation: I utilized ChatGPT to generate SPSS syntax for creating interaction terms in a 97 X 4 matrix (race by item). This was integral for conducting moderated multiple regression analyses, aiming to establish differential prediction based on continuous supervisor scores and binary supervisor status (1 = supervisor, 0 = non-supervisor).
II. Regression Analyses: The AI facilitated the development of large amounts of complex statistical analysis syntax for both binary logistic regression and multiple regression analyses, incorporating the interaction terms effectively.
III. Racial Codes Creation: Used the AI to write syntax that would generate unique racial codes based on participants' self-identifications from a "select all that apply" question, allowing for a nuanced analysis of racial group memberships.
IV. Consistency Comparison: Used AI to generate command syntax (for statistical software) to compare self-identified race and other protected class standings between two separate surveys, assessing consistency. The AI-generated syntax marked consistency (1) versus inconsistency (0) for each protected class.
12_outputs: The output was SPSS syntax and recommendations about configuring the syntax
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 9/16/2024
19_date_acq_dev_began: 9/16/2024
20_date_implemented: 9/16/2024
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: No
33_agency_data: none
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: No
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
45_compute_request: Other
45_question_type: Just access to the internet
47_timely_resources: Yes
47_question_type: no model training involved
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.
2_use_case_name: DSD Python Code Translation
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The AI purpose for this effort is to assist the translation of SAS to Python code for the DSD data processing and supporting systems. The effort is in support of the Census mission to migration to enterprise-wide solutions and reducing dependency on costly proprietary software products.
12_outputs: The expected AI final system outputs are the translated Python code that replicates the SAS code. Comparison of the system outputs will be performed until the expected results are achieved.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/2/2024
19_date_acq_dev_began: 10/28/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 1333LC
24_hisp_support: No
31_data_catalog: No
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.
35_demo_features: Other
35_question_type: Pre-trained model for translating SAS code
40_has_ato: No
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 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.
2_use_case_name: Census API GPT
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Other
8_question_type: Statistical Information about People, Places, and the Economy
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The Census API GPT provides guidance and assistance in using the Census API to retrieve a wide range of statistical information. It helps users form accurate API requests for demographic, economic, housing, and geographic data queries.
12_outputs: The Census API GPT offers clear explanations of the available datasets, parameters, and methods to efficiently retrieve the desired data, ensuring that users can access comprehensive information across these key areas. When necessary, it asks for clarification to ensure the accuracy and relevance of the request, making the process of using the Census API straightforward for users of all technical backgrounds. It emphasizes the importance of specificity in queries to reduce errors and improve the relevance of the data retrieved, catering to both beginners and experienced users alike. Additionally, the Census API GPT displays the data returned by the API, providing a direct view of the output alongside the guidance on constructing the API call.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/24/2024
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.
2_use_case_name: Natural Language Search for data.census.gov
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Other
8_question_type: Statistical Information about People, Places, and the Economy
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The current search algorithm on data.census.gov does not support true natural language search and is keyword-based. Leveraging a natural language search interface powered by an LLM that uses a RAG architecture to fetch responses to user queries has the potential to vastly improve the search experience and ensure that users of all backgrounds are able to quickly find the information they are looking for.
12_outputs: The AI system would consist of a natural language (LLM) input that would then use RAG (retrieval augmented generation) to identify an answer to the user's query (either in the form of an actual statistic, a relevant dataset, or relevant filters). Once an answer has been identified, it would be returned to the user via a natural language (LLM) interface.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/24/2024
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.
2_use_case_name: ACES CAPEX (structures, equipment, other) Machine Learning
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: The annual capital expenditures survey used to ask respondents to classify structures, equipment, or "other".  Machine Learning was implemented to classify these items better.  ACES is no longer an active survey, and the machine learning is no longer active.
12_outputs: Decision on if an item is structures or equipment.
16_dev_stage: Retired
17_impact_type: Neither
20_date_implemented: 1/1/2023
2_use_case_name: FirstNet Authority OCIO MS CoPilot Project
3_agency: Department of Commerce
3_abr: DOC
4_bureau: FIRSTNET - First Responder Network Authority
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Microsoft 365 Copilot can improve content creation and boost user productivity experience.  Microsoft 365 Copilot in the Microsoft Office apps will likely assist in creating content more quickly and improving existing content.  This helps the users to convey their ideas with reduced effort and unlocks the value of existing organization information and knowledge. 
12_outputs: Text and/or graphics generated by user-input.
16_dev_stage: Initiated
17_impact_type: Neither
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.
2_use_case_name: FirstNet Authority Communications Topaz Labs Photo Editing
3_agency: Department of Commerce
3_abr: DOC
4_bureau: FIRSTNET - First Responder Network Authority
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: Utilize AI photo and video editing tools to improve picture quality, smooth video, and scale images without degredation.
12_outputs: Digital photo and video files.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
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.
2_use_case_name: WAWENETS
3_agency: Department of Commerce
3_abr: DOC
4_bureau: National Telecommunications and Information Administration
8_question_type: The algorithm produces estimates of telecommunications speech quality and speech intelligibility.  The input is a recording of speech from a telecommunications system in digital file format.  The output is a single number that indicates speech quality (typically on a 1 to 5 scale) or speech intelligibility (typically on a 0 to 1 scale).
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
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.
2_use_case_name: Streamline Spectrum Activities
3_agency: Department of Commerce
3_abr: DOC
4_bureau: National Telecommunications and Information Administration
8_topic_area: Other
8_question_type: Spectrum Management
10_commercial_ai: None of the above.
11_purpose_benefits: Streamline NTIA Office of Spectrum Management (OSM) processes and enhance Federal spectrum management. This includes using Azure and CoPilot capabilities to reduce the level of effort and improve the timeliness of OSM and Federal spectrum management processes.
12_outputs: OSM CoPilot provides General office productivity outputs. Additional outputs include orchestration of current spectrum management functionality, speeding processes by eliminating clicks and keystrokes while under the supervision of  spectrum stakeholders.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 12/10/2024
19_date_acq_dev_began: 12/10/2024
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: Other
31_question_type: NTIA spectrum data exists but requires transformation to AI-ready / machine-readable format 
33_agency_data: NTIA Manual of Regulations and Procedures for Federal Radio Frequency Management
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: Does not use any demographic variables.
2_use_case_name: Spectrum Visualizations
3_agency: Department of Commerce
3_abr: DOC
4_bureau: National Telecommunications and Information Administration
8_topic_area: Science & Space
8_question_type: Creating visual representations of data sets for reports and presentations using AI.
16_dev_stage: Initiated
17_impact_type: Neither
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.
2_use_case_name: Use of Lexis. Other searches for legal research 
3_agency: Department of Commerce
3_abr: DOC
4_bureau: National Telecommunications and Information Administration
8_topic_area: Law & Justice
16_dev_stage: Initiated
17_impact_type: Neither
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.
2_use_case_name: Grants Program Administration
3_agency: Department of Commerce
3_abr: DOC
4_bureau: National Telecommunications and Information Administration
8_topic_area: Government Services (includes Benefits and Service Delivery)
11_purpose_benefits: Our team is exploring using AI to support our grant program administration, specifically supporting some AI-enable knowledge management for our internal team and supporting reviews of post award technical/progress/performance/financial reports to identify trends, identify inconsistencies, etc.  The latter may be more machine learning/compliance-based analytics and be on the fringe of AI.  
16_dev_stage: Initiated
17_impact_type: Neither
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.
2_use_case_name: FathomNet
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Science & Space
8_question_type: Ocean imagery
10_commercial_ai: None of the above.
11_purpose_benefits: Ocean image annotation and model building
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: Unknown
20_date_implemented: Ongoing
22_dev_method: Developed with contracting resources.
29_contains_pii: No
30_saop_review: No
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.
2_use_case_name: Coastal Change Analysis Program (C-CAP)
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Science & Space
8_question_type:  Landcover developement
10_commercial_ai: None of the above.
11_purpose_benefits: Develop high resolution land cover data for the United States.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 7/7/1905
19_date_acq_dev_began: 2015-2022
20_date_implemented: Ongoing
22_dev_method: Developed with both contracting and in-house resources.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Existing NOAA C-CAP and other U.S. government authoritative geospatial datasets.
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.
35_demo_features: Other
35_question_type: Does not use any demographic variables.
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/NOAA-OCM/CCAP
45_compute_request: Yes