GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Person-Centric Identity Services A-Number Management Model
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The aim of this use case is to leverage machine learning to test the accuracy of PCIS to identify and manage associations between individuals and their assigned A-numbers, which is a unique 7, 8, or 9 digit number assigned to a noncitizen by DHS.  The A-Number plays a critical role in surfacing of a person and all their associated records from across PCIS.
12_outputs: Numerical likelihood score which is used to determine the validity of the A# presented. Likelihood scores are subjected to a high threshold (.98, maximum 1) to assess whether the A# presented belongs to the individual.
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: 7/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QTCA22D00AB
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: USCIS-only data derived from 7 form-processing source systems including C3, ELIS, CPMS, GLOBAL, CIS2, AR-11, CAMINO.
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: Person Centric Identity Services
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 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.
2_use_case_name: Identity Match Option (IMO) Tool for Record Compilation
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: USCIS uses the IMO tool to aid in person-centric research and analytics. More specifically, IMO is used to derive a single identity across multiple systems for each applicant or beneficiary who interacts with USCIS.
12_outputs: A user friendly dashboard is created to display results and shows the data pattern but does not allow for any prediction or decision making. IMO is a COTS product offered by Informatica. The product has a variety of “transformations” that can be used together to build a workflow based solution. There are a number of different algorithms (Soundex, Jaro, Hamming distance, etc.) which are available for use in performing identity resolution.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/3/2017
19_date_acq_dev_began: 3/13/2017
20_date_implemented: 1/3/2018
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR21F00000194
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: N/A - The Agency does not own any of the data used to train, fine-tune, and/or evaluate performance of the model(s) used in this use case.
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
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: Other
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: Azure OpenAI Chat Prototype
3_agency: Department of Commerce
3_abr: DOC
4_bureau: BEA - Bureau of Economic Analysis
8_topic_area: Mission-Enabling
11_purpose_benefits: Provide staff with the ability to chat with an LLM model, comparable to ChatGPT, that is contained within BEA's network boundary.
12_outputs: Text and data anlysis
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/12023
22_dev_method: Developed with contracting resources.
23_contract_piids: 1331L524F13210282
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Outputs from the tool will be reviewed by a human for accuracy and reliability prior to incorporating text into a final document
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: N/A--We are doing no model training.
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: Does not use any demographic variables.
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: 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.
52_impact_assessment: No
53_real_world_testing: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
54_key_risks: If the outputs from the chat tool are not accurate and are not reviewed sufficiently by the human writing the prompt then the quality of the end document will be compromised.  
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: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: N/A; still in initiation phase
63_stakeholder_consult: None of the above
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: Individuals do not interact with and are not impacted by this use case.
67_opt_out: Other
67_question_type: Individuals do not interact with and are not impacted by this use case.
2_use_case_name: Sentinel One Purple AI
3_agency: Department of Commerce
3_abr: DOC
4_bureau: BEA - Bureau of Economic Analysis
8_topic_area: Mission-Enabling
11_purpose_benefits: Faster and more throughout incident response within the BEA cybersecurity program
12_outputs: Log analysis
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Outputs from the tool will be reviewed by a human for accuracy and reliability; actions based on data provided by the AI will be manual, not automated.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: N/A--We are doing no model training.
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: Does not use any demographic variables.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: N/A--COTS tool used for code conversion.
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.
52_impact_assessment: No
53_real_world_testing: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
54_key_risks: If the outputs of the AI analysis of BEA logs is not accurate then the timeliness and accuracy of BEA's incident response program will suffer
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: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: N/A; still in initiation phase
63_stakeholder_consult: None of the above
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: Individuals do not interact with and are not impacted by this use case.
67_opt_out: Other
67_question_type: Individuals do not interact with and are not impacted by this use case.
2_use_case_name: Real Time Classification for the Economic Census
3_agency: Department of Commerce
3_abr: DOC
4_bureau: CENSUS - U.S. Census Bureau
8_topic_area: Other
8_question_type: Classification
10_commercial_ai: None of the above.
11_purpose_benefits: Two ML models (BEACON and SINCT) are applied in this Economic Census use case to perform real-time classification of Primary Business Activity (PBA), or NAICS, and Products, or NAPCS. For instance, in the 2017 Economic Census, if respondents could not identify their NAICS or NAPCS codes, they were able to provide a short, open-ended description of their business and the products and services they offer, referred to as “write-ins”. There were nearly 400,000 NAICS write-ins and 1,000,000 NAPCS write-ins, out of 3 million and 18 million total reported NAICS and NAPCS codes, respectively. While the number of write-ins is a small share of the total number of reported NAICS and NAPCS codes, these write-ins were previously assigned to a valid code by analysts using manual processes. BEACON and SINCT were developed to improve classification accuracy and reduce processing time and costs by providing real-time feedback to respondents given the business description they provide. In the 2022 Economic Census, as a result of BEACON and SINCT, NAICS and NAPCS write-ins were reduced by 58% and 78% to 168,000 and 220,000 write-ins, respectively.
12_outputs: For PBA code reporting, the respondent has an initial set of NAICS codes to choose from.  If the respondent selects "Other" with a write-in, an API call to BEACON returns predictive results from the model with a ranked list of predicted NAICS options from which to select.  For Products, if the respondent selects to "Add products", an API call to SINCT provides predictive results of products to add. In both cases, the ML model recommends codes based on scoring attributes, and the respondent is able to choose a relevant NAICS or NAPCS code from those results.
16_dev_stage: Retired
17_impact_type: Neither
20_date_implemented: 1/1/2023
2_use_case_name: Automated Change Detection
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: As part of the Census Bureau's efforts to maintain and update the Geographic Frame ahead of the 2030 Decennial Census, the Geography Division is using AI to identify changes in the built environment throughout the nation twice a year. With the areas of change we are extracting our own geographic features (roads, building footprints) to support improving coverage across the nation.
12_outputs: We use Google Earth Engine to analyze change in construction using Sentinel-2 imagery and recommend where to use high resolution imagery. The object detection provides roads and building footprints to improve our feature coverage.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/4/2021
19_date_acq_dev_began: 5/24/2023
20_date_implemented: 8/1/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 47QTCA19D00MP
24_hisp_support: Yes
25_hisp_name: United States Census Bureau
26_public_service: Obtaining Census Bureau statistics online
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The high resolution imagery for training our building footprints and road predictions is from Maxar, USG Plus through National Geospatial-Intelligence Agency Global Enhanced GEOINT Delivery system. We are evaluating extracted features produced from the high resolution imagery and fine-tuning the output with staff to correct any inconsistencies.
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: Google Cloud Platform and Enterprise Data Lake
42_dev_tools_wait: 6-12 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: School staff information extraction from web page text
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: This AI use case examines publicly available information on the web related to staff rosters at public and private schools. The goal is to improve quality and coverage of Teacher and Principal frame.
12_outputs: The AI in this use case predicts whether a link on a webpage leads to a staff directory and extracts person names and teacher titles from webpage text.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/15/2021
19_date_acq_dev_began: 1/15/2021
20_date_implemented: 5/10/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Brite Group
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: School employment lists and web scraped data.
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: 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 Bureau Demographic Frame Person-Place Model
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 Demographic Frame’s Person-Place Model (PPM) uses machine learning to assign individuals to their most accurate residential locations. This model addresses a critical need for accurate “person-place” associations, enabling the Census Bureau to support a range of essential use cases—from demographic analyses to population estimates. By leveraging machine learning, the PPM generates predicted probabilities for each person-address record, refining location accuracy while accommodating diverse data sources. This innovation strengthens the Census Bureau’s mission to deliver reliable, high-quality data, benefiting both governmental operations and the general public by enhancing data precision, policy-making, and service delivery.
12_outputs: The Person-Place Model (PPM) in the Demographic Frame outputs predicted probabilities indicating the likelihood that a person resides at a specific address. These predictions are generated by a machine learning model that analyzes historical and current data to produce a probability score for each person-address record. These scores do not constitute a final decision or assignment; instead, they provide a flexible basis for determining residential locations based on the desired level of confidence or specific application needs. This output can be used as input to downstream processes, providing robust, data-driven estimates to support various Census Bureau use cases and enhance the accuracy of demographic analyses.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/28/2021
19_date_acq_dev_began: 10/28/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1331L523D13OS0003
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: American Community Survey data
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
Race/Ethnicity
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: Race and Ethnicity Autocoding
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 aims to develop an automated methodology for coding race and ethnicity write-in entries. The goal is to improve quality and efficiency in the coding of race and ethnicity write-in entries.
12_outputs: The AI produces a predicted set of standardized race and ethnicity concept codes that align with the write-in response.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 12/1/2021
19_date_acq_dev_began: 2/15/2022
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Response and clerical data.
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: Race/Ethnicity
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: Information extraction for web scraped data for Group Quarters frame enhancement
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 examines publicly available information on the web related to a variety of Group Quarters (GQ) types. The goal is to improve quality and coverage of GQ frame.
12_outputs: Prediction label that corresponds to one of the following : extracted capacity of the GQ, extracted address component of the GQ, and extracted concepts, such as dates and building names, identified from pretrained NER models.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 12/1/2021
19_date_acq_dev_began: 5/16/2022
20_date_implemented: 8/16/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Brite Group
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Address lists and web scraped data.
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: 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.