GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Sustained Casing Pressure Identification
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BSEE
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Well casing pressure requests are submitted to BSEE to determine whether a well platform is experiencing a sustained casing pressure (SCP) problem. SCP is usually caused by gas migration from a high-pressured subsurface formation through the leaking cement sheath in one of the wells casing annuli, but SCP can also be caused by defects in tube connections, downhole accessories, or seals. Because SCP can lead to major safety issues, quickly identifying wells with SCP could greatly mitigate accidents on the well platforms. BSEE entered into an Inter-Agency Agreement with NASA's Advanced Supercomputing Division to help research the use of various AI techniques.
12_outputs: Sustained Casing Pressure Prediction
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2018
19_date_acq_dev_began: 3/24/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: IAA E21PG00001
24_hisp_support: No
27_public_info: No
28_iqa_compliance: IQA not applicable. Project is for research and proof of concept, not in production.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Most of the data needed is cataloged but some data points are not.
33_agency_data: Currently no agency owned data is being used to train and test the model.
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: Other
43_question_type: Project is being developed outside BSEE's IT infastructure.
45_compute_request: Other
45_question_type: Project is being developed outside BSEE's IT infastructure.
47_timely_resources: Other
47_question_type: Project is being developed outside BSEE's IT infastructure.
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: ROV Smart Touch Subsea Pipeline Inspections
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BSEE
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The BSEE funded project with the University of Houston is researching the development a state-of-the-art robotic system, "Smart Touch," to enhance subsea pipeline inspections by integrating advanced robotics and machine learning technologies. The system uses Remote Operated Vehicles (ROVs) equipped with stress wave-based smart touch sensors, video cameras, and scanning sonars to detect flange loosening and pipeline leaks autonomously. By leveraging machine learning for active sensing and robotic navigation, the project aims to improve inspection efficiency, reduce reliance on human operators, and mitigate risks associated with subsea operations. Collaborations with industry will support development, testing, and potential commercialization.
12_outputs: Bolt and flange tightness level prediction
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/27/2023
19_date_acq_dev_began: 7/27/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: Firm Fixed Price 140E0123C0007
24_hisp_support: No
27_public_info: No
28_iqa_compliance: IQA not applicable. Project is for research and proof of concept, not in production.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Most of the data needed is cataloged but some data points are not.
33_agency_data: Data in oracle tables and unstructured attachment 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.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Project is being developed outside BSEE's IT infastructure.
45_compute_request: Other
45_question_type: Project is being developed outside BSEE's IT infastructure.
47_timely_resources: Other
47_question_type: Project is being developed outside BSEE's IT infastructure.
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: Form Recognition model for Benefits Forms
3_agency: Department of Labor
3_abr: DOL
4_bureau: OWCP
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Custom machine learning model to extract data from complex forms to tag data entries to field headers. The input is a document or scanned image of the form and the output is a JSON response with key/value pairs extracted by running the form against the custom trained form recognition model.
12_outputs: The AI system output is a recommendation.
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: 3/1/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1605DC19A0004
NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: No specific dataset. The AI engine came ready off the shelf, and the final product has been tested ad hoc by agency users and their NLP free text data. 
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Azure
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: Language Translation
3_agency: Department of Labor
3_abr: DOL
4_bureau: Department Wide Initiative
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: To automatically translate unofficial documents into various languages quickly and easily, without the time and expense needed for human translation. 
12_outputs: Language translation of published documents and websites using natural language processing models.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/1/2023
2_use_case_name: Audio Transcription
3_agency: Department of Labor
3_abr: DOL
4_bureau: OWCP||VETS||WHD
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Transcription of speech to text for records keeping using natural language processing models.
12_outputs: Text transcription
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/22/2023
19_date_acq_dev_began: 3/22/2023
20_date_implemented: 3/22/2023
22_dev_method: Developed with contracting resources.
23_contract_piids:  NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Agency datasets are not necessary for the development of this use case.
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Azure
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: 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: Yes
2_use_case_name: Text to Speech Conversion
3_agency: Department of Labor
3_abr: DOL
4_bureau: EBSA||OHR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Text to speech (Neural) for more realistic human sounding applications using natural language processing models.
12_outputs: 

Audio files used on Power point presentations,
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/22/2023
19_date_acq_dev_began: 1/22/2023
20_date_implemented: 1/22/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: NO
31_data_catalog: Other
31_question_type: Agency datasets are not necessary for the development of this use case.
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Azure
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: 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: Yes
2_use_case_name: Claims Document Processing
3_agency: Department of Labor
3_abr: DOL
4_bureau: OWCP
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: To identify if physician's note contains causal language by training custom natural language processing models.
12_outputs: Document ranking by likely relevance and sentences highlighting of potentially causal language.
16_dev_stage: Retired
17_impact_type: Rights-Impacting

21_date_retired: 7/16/2024
2_use_case_name: Website Chatbot Assistant
3_agency: Department of Labor
3_abr: DOL
4_bureau: OTAA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The chatbot helps the end user with basic information about the program, information on who to contact, or seeking petition case status.
12_outputs: 

More than AI, it is question and answer bot based on the pre determine question and answers, there is no AI which would give any out put
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/22/2023
19_date_acq_dev_began: 4/23/2023
20_date_implemented: 5/10/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids:  NNG15SD22B
1605DC19A0004
24_hisp_support: NO
27_public_info: Yes
28_iqa_compliance: The chatbot’s Question and Answer knowledge base was developed by expert staff members that regularly handle information requests and inquires from the public about the TAA program. These experts identified the most frequently asked questions as well as the relevant answers to these. There is a Feedback form for the website in close proximity to the chatbot window. Users can give feedback on the bot there. The chatbot also provides direct email and phone contact info for the TAA program, where users can let staff know if there are any issues or incorrect/incomplete info.
29_contains_pii: No
30_saop_review: NO
31_data_catalog: NO
33_agency_data: Frequently asked questions and their answers pertaining to the TAA program.
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes
39_code_link: https://aws.amazon.com/lex/
40_has_ato: Yes
41_system_name: AWS / DOL.gov
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: Yes
2_use_case_name: Data Ingestion of Payroll Forms
3_agency: Department of Labor
3_abr: DOL
4_bureau: WHD
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Custom machine learning model to extract data from complex forms to tag data entries to field headers. The input is a document or scanned image of the form and the output is a JSON response with key/value pairs
12_outputs: 
The output is a JSON response with key/value pairs extracted by running the form against the custom trained model.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 8/1/2023
21_date_retired: 1/1/2024
2_use_case_name: Hololens
3_agency: Department of Labor
3_abr: DOL
4_bureau: OSHA
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: AI used to train Inspectors to visually inspect  unsafe areas from a safe location.
12_outputs: 

Pictures of the location
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/21/2023
19_date_acq_dev_began: 8/21/2023
20_date_implemented: 8/21/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids:  1605TA20C0001
24_hisp_support: No
27_public_info: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Purchased
34_data_docs: Documentation is missing or not available
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
2_use_case_name: DOL Intranet Website Chatbot Assistant
3_agency: Department of Labor
3_abr: DOL
4_bureau: OSPE
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 
Conversational AI Assistant & DOL intranet websites to help answer common procurement questions, as well as specific contract questions.
12_outputs: It will answer question based on the question pertaining to the knowledge base.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2023
19_date_acq_dev_began: 8/1/2023
20_date_implemented: 10/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1605DC19A0004
NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Agency Generated
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
41_system_name: Azure
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: Official Document Validation
3_agency: Department of Labor
3_abr: DOL
4_bureau: OWCP
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: AI detection of mismatched addresses and garbled text in official letters sent to benefits recipients.
12_outputs: 

The output is a recommendation for review of mismatched a addresses and garbled text in official letters sent to benefits recipients.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 2/16/2024
2_use_case_name: Electronic Records Management
3_agency: Department of Labor
3_abr: DOL
4_bureau: Department Wide Initiative
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Meeting NARA metadata standards for (permanent) federal documents by using AI to identify data within the document, and also using NLP to classify and summarize documents.
12_outputs: 

The system output is decision based and summarization of documents.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 12/22/2023
2_use_case_name: Call Recording Analysis
3_agency: Department of Labor
3_abr: DOL
4_bureau: EBSA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 

Automatic analysis of recorded calls made to Benefits Advisors in the DOL Interactive Voice Response (IVR) center. AI is not used for analysis, AI is used only for transcription
12_outputs: 

The output is a transcription of calls. The AI is only doing the transcriptions
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/23/2023
19_date_acq_dev_began: 4/1/2024
20_date_implemented: 5/24/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1605DC19A0004
NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: NO
31_data_catalog: Other
31_question_type: Agency datasets are not necessary for the development of this use case.
33_agency_data: Agency owned data to do analysis of call recordings
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: Yes
40_has_ato: Yes
41_system_name: Azure
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: Yes
2_use_case_name: Automatic Document Processing
3_agency: Department of Labor
3_abr: DOL
4_bureau: OWCP
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Automatic processing of continuation of benefits form to extract pre-defined selection boxes. AI tool will extract data from the forms.
12_outputs: 

AI tool will extract data from the forms for further processing
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/23/2023
19_date_acq_dev_began: 2/23/2023
20_date_implemented: 5/29/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1605DC19A0004
 NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Agency Generated
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Azure
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: Yes
2_use_case_name: Automatic Data Processing Workflow with Form Recognizer 
3_agency: Department of Labor
3_abr: DOL
4_bureau: ETA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Automatic processing of current complex workflow to extract required data. 
12_outputs: The output of the AI system is a consolidated data for review. The GPT will extract the data and into a spreadsheet and gets reconciled. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/22/2023
19_date_acq_dev_began: 11/30/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: NNG15SD22B, 1605DC19A0004
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Historical ETA-9130/ETA-9178 forms, 9178/9130 data from R1 states.
34_data_docs: Documentation is widely available
40_has_ato: Yes
41_system_name: Azure
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
43_question_type: n/A
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: 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: Case Recording summarization
3_agency: Department of Labor
3_abr: DOL
4_bureau: OSHA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Using an open source large language model to summarize publicly available case recording documents which are void of personal identifiable information (PII) or any other sensitive information. This is not hosted in the DOL technical environment and is reviewed by human note takers.
12_outputs: Summarized version of text
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 4/23/2023
2_use_case_name: OEWS Occupation Autocoder
3_agency: Department of Labor
3_abr: DOL
4_bureau: BLS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The input is state submitted response files that include occupation title and sometimes job description of the surveyed units. The autocoder reads the job title and assigns up to two 6-digit Standard Occupational Classification (SOC) codes along with their probabilities as recommendations for human coders. Codes above a certain threshold are appended to the submitted response file and sent back to states to assist them with their SOC code assignment.
12_outputs: 6-digit Standard Occupational Classification (SOC) codes
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2015
19_date_acq_dev_began: 1/1/2015
20_date_implemented: 1/1/2018
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: Agency Generated
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: BLS Internal system
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: Yes