GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Develop a Custom AI Model to Power Generative AI- Capabilities
3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: NARA plans to build a custom AI model trained on its own data, which will improve the performance of its AI applications. This will lead to more accurate and relevant results, a better user experience, and new possibilities for using AI in managing and accessing records.
12_outputs: This AI use case will output an AI model that is specifically trained on NARA's data and designed to meet NARA's unique needs.
16_dev_stage: Planned
17_impact_type: Neither
41_system_name: TBD
2_use_case_name: Develop a Natural Language Based Chat Interface (like ChatGPT) to Interact With the Archival Documents
3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: NARA plans to develop an AI-powered chat interface, similar to ChatGPT, that will allow users to easily explore its digital archives by asking questions in everyday language. This will make historical documents more accessible and engaging, leading to more efficient research and a deeper understanding of history.
12_outputs: This AI will create a chat interface that lets users easily get information from NARA's archives by having a conversation with it.
16_dev_stage: Planned
17_impact_type: Neither
41_system_name: TBD
2_use_case_name: Topic Summarizer and Entity Extraction using AI

3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: NARA plans to use AI to automatically create descriptions for its digital objects. This will free up archivists' time and make it easier for people to find and understand the digital materials in NARA's system.
12_outputs: This AI use case will output enriched metadata making NARA's digital assets more accessible and easier to manage.
16_dev_stage: Planned
17_impact_type: Neither
41_system_name: TBD
2_use_case_name: Automated Data Discovery and Classification Pilot 
3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: NARA plans to test how well AI can automatically organize and categorize its data. This pilot project will help NARA understand its data better, find information faster, improve efficiency, and manage risks more effectively.
12_outputs: This AI pilot will categorize data, improve search, and potentially train AI to identify new document types.
16_dev_stage: Planned
17_impact_type: Neither
41_system_name: TBD
2_use_case_name: Voice to text transcription
3_agency: National Transportation Safety Board
3_abr: NTSB
4_bureau: NTSB
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Create draft voice to text transcription of investigation recordings, improving speed and efficiency of initial processing for audio recordings prior to manual transcription
12_outputs: Draft transcription
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/11/2024
2_use_case_name: Semantic Similarity Text Search
3_agency: National Transportation Safety Board
3_abr: NTSB
4_bureau: NTSB
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Semantic search of agency library of safety recommendation and report texts, improving speed and efficiency of researching previously identified transportation safety topics
12_outputs: Search results identifying semantically similar narrative in previously published reports and safety recommendations
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
2_use_case_name: Synthetic Data Generation
3_agency: Pension Benefit Guaranty Corporation
3_abr: PBGC
4_bureau: Office of Information Technology (OIT) 
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: To create artificial data that mimics the features, structures, and statistical attributes of end user/pensioner data
12_outputs: Unlike real data, which may contain sensitive or Personally Identifiable Information (PII), the synthetic data ensures data privacy, while enabling data analysis, research, and software testing
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/1/2022
21_date_retired: N/A 
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
26_public_service: Using website resources to assist organizations with compliance
27_public_info: No
28_iqa_compliance: N/A 
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: N/A.  Currently conducting market research to identify requirements for acquisition/implementation. 
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
41_system_name: N/a.  Acquisition has not been completed as of yet. 
43_infra_provisioned: No
45_compute_request: No
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: Planned or in-progress.
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: TBD.  Currently doing market research to assess potential risks involved with deployment. 
55_independent_eval: Yes – by the CAIO
57_question_type: Market researched still being conducted to determine configuration.  
59_ai_notice: None of the above
63_stakeholder_consult: None of the above
66_no_appeal_reason: Market research is still being conducted.
2_use_case_name: Lexis +AI
3_agency: Postal Regulatory Commission
3_abr: PRC
4_bureau: OGC
8_topic_area: Law & Justice
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Legal Research
12_outputs: Research
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 7/1/2024
20_date_implemented: 7/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Sole Source
24_hisp_support: Yes
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
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
41_system_name: Lexis +AI
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: 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.
52_impact_assessment: No
2_use_case_name: Productivity and communications including the generation of insights, automation and workflow
3_agency: Presidio Trust
3_abr: PT
4_bureau: Information Technology
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Improved productivity, collaboration, information and communications
12_outputs: Communications, recommendations, information
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Ability to find information through search results and other methods
3_agency: Presidio Trust
3_abr: PT
4_bureau: Information Technology
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Improved finding of information
12_outputs: Search results, translation, questions answered
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Pattern identification, simulations, optimization and forecasting leading to alerts, notifications and reporting
3_agency: Presidio Trust
3_abr: PT
4_bureau: Information Technology
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Improved awareness of the environment, better forecasting of future events
12_outputs: Insights, warnings, alerts, recommendations
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Scheduling and managing social media posts using AI, including sentiment analysis
3_agency: Presidio Trust
3_abr: PT
4_bureau: Information Technology
8_topic_area: Mission-Enabling
10_commercial_ai: Scheduling and managing social media posts using AI.
11_purpose_benefits: Improved communications, sentiment analysis, awareness and insights
12_outputs: Reports on social media, recommended communications
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Optimized navigation for traffic and other purposes
3_agency: Presidio Trust
3_abr: PT
4_bureau: Information Technology
8_topic_area: Mission-Enabling
10_commercial_ai: Planning travel routes using AI-driven map applications.
11_purpose_benefits: Improved navigation and routing
12_outputs: Navigation and routing
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Comment Letter Review and Analysis 
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Office of Information Technology (OIT)
8_topic_area: Other
8_question_type: Securities & Financial Markets
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Enhances the review of public comment letters.
12_outputs: Comment letter summaries.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 22-Jul
19_date_acq_dev_began: 22-Dec
20_date_implemented: 24-Jun
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 50310220F0059
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Comment letter 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: Other
35_question_type: Not Applicable
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: Comment Letter Review and Analysis System
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: Forms Filed
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Division of Investment Management (IM)
8_topic_area: Other
8_question_type: Securities & Financial Markets
10_commercial_ai: None of the above.
11_purpose_benefits: Assists staff in evaluating completeness of filed information at scale by automating the identification of failures to file certain forms utilized by investment companies.
12_outputs: In planning phase - TBD
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 24-Oct
2_use_case_name: Federal Acquisition Research Solution
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Office of Acquisitions (OA)
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Aggregates and summarizes federal acquisition policy, best practices, and templates for common issues raised for the federal acquisition workforce and stakeholders. This tool will support OA operations, training, and policy formulation.
12_outputs: AI-generated text aggregating and summarizing federal acquisition policy, best practices, and templates for common acquisition issues.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 24-Aug
19_date_acq_dev_began: 24-Aug
20_date_implemented: 24-Aug
22_dev_method: Developed with contracting resources.
23_contract_piids: 50310224F0144
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Not Applicable
33_agency_data: Not Applicable
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: Not Applicable
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: Yes
45_compute_request: Other
45_question_type: Not Applicable
47_timely_resources: Other
47_question_type: Not Applicable
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: Human Resources Search
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Office of Human Resources (OHR)
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Provides results to staff inquiries. 
12_outputs: Search results identifying knowledge articles and services available.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 22-Nov
19_date_acq_dev_began: 22-Apr
20_date_implemented: 22-Nov
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 503102-22-F-0107
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Knowledge articles related to human resource information
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: Not Applicable
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: ServiceNow HR Service Delivery 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: Not Applicable
47_timely_resources: Other
47_question_type: Not Applicable
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: 8-K Filing Solution
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Division of Corporate Finance (CF)
8_topic_area: Other
8_question_type: Securities & Financial Markets
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Searches and extracts information from certain 8-K filings (HTML files and images).
12_outputs: Extracts of filing data.

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 24-Aug
19_date_acq_dev_began: 24-Sep
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Not Applicable
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: Not Applicable
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.
2_use_case_name: Training Conversation Tool 
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Office of Human Resources (OHR)
8_topic_area: Education & Workforce
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Improves communication and collaboration skills of the SEC workforce
12_outputs: Feedback on collaboration and communication.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 24-Nov
19_date_acq_dev_began: 24-Nov
20_date_implemented: 24-Nov
22_dev_method: Developed with contracting resources.
23_contract_piids: NNG15SC71B-50310224F0140
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Not Applicable
33_agency_data: Not Applicable
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: Not Applicable
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: Yes
45_compute_request: Other
45_question_type: Not Applicable
47_timely_resources: Other
47_question_type: Not Applicable
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.
2_use_case_name: Mobile Phone Artificial Intelligence Features
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Office of Information Technology (OIT)
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Tests new mobile phone AI features. 
12_outputs: In planning phase - TBD
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 24-Nov
2_use_case_name: Create Work-Related Videos
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Office of Human Resources (OHR)
8_topic_area: Other
8_question_type: Securities & Financial Markets
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: Allows staff to make and edit work-related videos. 
12_outputs: A video is created, which may be edited.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 24-Nov
19_date_acq_dev_began: 23-Jun
20_date_implemented: 24-May
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QTCA21D007B/50310223F0119/P00001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Not Applicable
33_agency_data: Not Applicable
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: Not Applicable
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Vyond
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: Not Applicable
47_timely_resources: Other
47_question_type: Not Applicable
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: CF Staff Review Comments 
3_agency: Securities and Exchange Commission
3_abr: SEC
4_bureau: Division of Corporate Finance (CF)
8_topic_area: Other
8_question_type: Securities & Financial Markets
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Classifies staff comments and improve CF's review process.
12_outputs: SEC staff comment categories
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 24-Sep
19_date_acq_dev_began: 24-Sep
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Filings review data
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: Not Applicable
40_has_ato: Yes
41_system_name: DREAM - Disclosure Review Automation
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.