GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Customer Insight Tool
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of Transformation
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Survey analysis tool that evaluates sentiment and categorization.
Current capabilities include: 
1. Machine Translation - using algorithms to automatically translate text from one language to another
2. Sentiment Analysis - classifies text into negative, positive and neutral sentiment
3. Categorization - system-generated groupings of conceptually-related words and phrases based on phrase-level feedback
4. Suggested Actions - uncovers explicit suggestions from feedback text
5. Customer Effort - aimed to recover explicit mentions of effort in feedback text
12_outputs: Survey analysis tool that evaluates sentiment and categorization.
Current capabilities include: 
1. Machine Translation - using algorithms to automatically translate text from one language to another
2. Sentiment Analysis - classifies text into negative, positive and neutral sentiment
3. Categorization - system-generated groupings of conceptually-related words and phrases based on phrase-level feedback
4. Suggested Actions - uncovers explicit suggestions from feedback text
5. Customer Effort - aimed to recover explicit mentions of effort in feedback text
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2021
19_date_acq_dev_began: 9/26/2020
20_date_implemented: 8/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: Inter-Agency Agreement (IAA-21-0017)
24_hisp_support: Yes
25_hisp_name: Social Security Administration - SSA
26_public_service: Obtaining audit disability benefits
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: No Agency data used for 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. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Customer Feedback Tool (CXFT)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: Third Party Vendor
47_timely_resources: Other
47_question_type: Third Party Vendor 
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: Benefit and Earnings Public Use File
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of Retirement and Disability Policy
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Generate high fidelity synthetic benefits and earnings data for external researches to support policy research and decision making while protecting privacy. 
12_outputs: Synthetic microdata file aimed for public use by policy researchers
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Comprehensive Data Quality Review
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Model used data from disability program data sets
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: Sex, Age, Marital Status, Earnings
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
43_infra_provisioned: No
43_question_type: 0
45_compute_request: No
47_timely_resources: Other
47_question_type: None requested
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: Therapy Chatbot - Text-Based Mental Health Support for SSA Employees
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of Human Resources
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case is a therapy chatbot that employees can access on their own personal devices.
12_outputs: Synthetic text output that serves discussion with users
16_dev_stage: Operation and Maintenance
17_impact_type: Safety-Impacting
19_date_acq_dev_began: 7/1/2023
20_date_implemented: 9/1/2023
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
31_data_catalog: No
33_agency_data: No Agency data used for 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. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
45_compute_request: Other
45_question_type: Third Party Vendor
47_timely_resources: Other
47_question_type: Third Party Vendor 
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: Yes – Agency requested an extension for this use case.
2_use_case_name: Agency Support Companion (ASC) chatbot
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of the Chief Information Officer
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Generative AI model to create content, summarization and research capabilities
12_outputs: Chatbot responses to users as well as management information reports for executives
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/15/2024
19_date_acq_dev_began: 8/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: ITSSC
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: No Agency data used for 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. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
41_system_name: Agency Support Companion
43_infra_provisioned: Yes
45_compute_request: No
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: Intelligent generation of modernized code.
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of the Chief Information Officer
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The module uses AI techniques such as cluster analysis, pattern matching, templates, and programming language models to generate modernized code in a new target language.
These AI techniques enable it to perform efficient data analysis, processing, and intelligent generation of new code. AI powered approach provides structured standardized code that’s highly maintainable.



12_outputs: Transformed legacy code into modernized language
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 7/1/2024
20_date_implemented: 11/1/2014
22_dev_method: Developed with contracting resources.
23_contract_piids: ITSSC
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: No Agency data used for 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. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
41_system_name: ISAT
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
45_question_type: Third Party Vendor
47_timely_resources: Other
47_question_type: Third Party Vendor
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: Data Governance Product (DGP) -  Data cataloging and predictive analysis of data assets. 
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of the Chief Information Officer
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Vendor built in AI engine for data cataloging to automates the discovery, classification, and curation of data assets. And also provides predictive insights for data asset analytics. Uses Natural Language Processing (NLP) enables non-technical stakeholders to contribute to rule creation and facilitates the conversion of plain text into regular expression.

12_outputs: Efficient data catalog search results. Plain English conversation with the users to create technical regular expression rule creation
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2020
19_date_acq_dev_began: 7/11/2022
20_date_implemented: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: contract number GS-35F-267DA
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Model used data from internal sources for performance and evaluation purposes
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Data Governance Product (DGP)
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: Product Service Code Automation ML Model
3_agency: Department of State
3_abr: STATE
4_bureau: A/GO
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Developed a proof-of-concept machine learning model to scan unstructured, user entered procurement data such as Requisition Title and Line Descriptions to automatically detect the commodity and service types being purchased for enhanced procurement categorization. This project is retired.
12_outputs: N/A it was a proof of concept that has been cancelled.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/1/2021
2_use_case_name: Tailored Integration Logistics Management System (ILMS) User Analytics
3_agency: Department of State
3_abr: STATE
4_bureau: A/GO
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Piloted the use of ILMS transactional data and planned transactions to develop tailored user experiences and analytics to meet the specifics needs of the user at that moment. This allowed for the extraction of more meaningful information about users by mining real system actions and clicks. This project is retired.
12_outputs: N/A this was a pilot project that is not in production and was cancelled. 
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 6/1/2022
20_date_implemented: 6/1/2022
21_date_retired: 6/14/2022
2_use_case_name: Property and Procurement Analytics
3_agency: Department of State
3_abr: STATE
4_bureau: A/GO
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Plan to expand current analytics within the Integrated Logistics Management System (ILMS) through development of a machine learning model for detecting patterns of potential anomalous activity in property and procurement.
12_outputs: Plan to have data on potential anomalous activity.
16_dev_stage: Planned
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 6/1/2022
2_use_case_name: AI input in Translation
3_agency: Department of State
3_abr: STATE
4_bureau: A/PRI/LS
8_topic_area: Mission-Enabeling 
10_commercial_ai: None of the above.
11_purpose_benefits: Leveraging machine-based tools to streamline workflows in translation work. Machine output is carefully post-edited by professional human translators to ensure highest quality product. 
12_outputs: Very rough suggested language that is carefully reviewed and edited by human professional translators. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/19/2024
19_date_acq_dev_began: 1/19/2024
20_date_implemented: 1/19/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
25_hisp_name: No
29_contains_pii: No
33_agency_data: In-house curated translation memory for training and fine-tuning models
43_infra_provisioned: Yes
49_existing_reuse: No
2_use_case_name: FOIA Web ML Document Indexer
3_agency: Department of State
3_abr: STATE
4_bureau: A/PRI/TI
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Development is underway for a machine learning algorithm to generate index metadata for the documents in the Department's online Freedom of Information Act (FOIA) Library, thereby improving document classification and accuracy of search. 
12_outputs: Plan to have a series of prepopulated metadata
16_dev_stage: Planned
17_impact_type: Neither
18_date_initiated: 2/27/2023
2_use_case_name: BudgetChat AI Tool
3_agency: Department of State
3_abr: STATE
4_bureau: BP/RPBI
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: BudgetChat (aka BudgetSearch) is expected to consolidate budget documents to make searching and summarizing them easier, thereby streamlining access and analysis of initiatives over multiple years and programs.
12_outputs: Summary of information that will be verified, reviewed, and edited by humans.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/26/2024
19_date_acq_dev_began: 9/12/2024
20_date_implemented: 12/1/2024
22_dev_method: Developed with both contracting and in-house resources.
33_agency_data: Budget documents that have been published from previous and current year. 
34_data_docs: Yes
43_infra_provisioned: Yes
47_timely_resources: Yes
2_use_case_name: Travel.state.gov (TSG) Content Refinement with AI Text Editor
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other
8_question_type: Consular Affairs
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: To assist in rewriting Public Facing content on International Travel Safety & Security to improve readability.
12_outputs: Content rewritten in a simplified manner that will be reviewed and edited by humans. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 5/1/2024
2_use_case_name: Predictive Analytics Platform
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other
8_question_type: Consular Affairs
11_purpose_benefits: Planned AI Use Cases that will be on the Predictive Analytics platform. The Predictive Analytics platform facilitates AI R&D and AI modeling, LLMs with services like Azure AI services, Azure Machine Learning, Azure Databricks, Azure Synapse and Data Science Virtual Machines etc. Individual AI Use Cases will be on this platform. 
12_outputs: The planned platform to provide the capacity to R&D AI Use Cases.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/15/2024
19_date_acq_dev_began: 8/12/2024
22_dev_method: Developed with both contracting and in-house resources.
43_infra_provisioned: Yes
2_use_case_name: Consular Affairs Photo Quality Service (PQS) / FaceVACS
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other
8_question_type: Consular Affairs
10_commercial_ai: None of the above.
11_purpose_benefits: To automatically check passport photo quality during the Online Passport Renewal (OPR) process, providing instant feedback to ensure submitted images meet requirements. Applicants are prompted to retake and upload new photos if needed to meet requirements or submit a physical photo through standard processes if an acceptable digital photo cannot be obtained.
12_outputs: Improved outcomes.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
22_dev_method: Developed with both contracting and in-house resources.
2_use_case_name: Innovation and Transformation Measurement and Prediction
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other; Mission-Enabling (internal agency support)
8_question_type: Consular Affairs
11_purpose_benefits: To measure and understand causal impacts of consular service changes and improvements on desired outcomes (e.g., efficiency, effectiveness, customer and employee experiences, actionable behaviors, etc.) using methods such as machine learning and statistical modeling. This will allow Consular Affairs to better predict how these service changes will impact customers and employees before they are implemented.
12_outputs: Improved outcomes.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
2_use_case_name: Evaluating Customer Feedback and Sentiments with AI
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other; Mission-Enabling (internal agency support)
8_question_type: Consular Affairs
11_purpose_benefits: To leverage Natural Language Processing (NLP) and secure Large Language Models (LLM) on unstructured text data to identify actionable insights to drive customer improvement initiatives. 
12_outputs: Sentiment analysis, categorization, and summarization of unstructured text data.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
2_use_case_name: Translation of Consular Content using AI
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other
8_question_type: Consular Affairs
11_purpose_benefits: To leverage AI translation models to increase CA's capacity to provide consular content in our websites to customers in their preferred language while ensuring accuracy and understanding, and reducing the resources typically needed at post.
12_outputs: Translated text
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: Travel.State.Gov (TSG) Enhanced Search and Chatbot  
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other
8_question_type: Consular Affairs
11_purpose_benefits: A new chatbot and enhanced search on the TSG website processing existing FAQs and resources. The chatbot provides predefined responses. 
12_outputs: Improved outcomes.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 2/1/2024
22_dev_method: Developed with both contracting and in-house resources.
33_agency_data: Data is posted on the site at www.travel.state.gov
34_data_docs: The data is public facing and documented in www.travel.state.gov.
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.
2_use_case_name: CodeGen - AI-assisted IT Application Development
3_agency: Department of State
3_abr: STATE
4_bureau: CA/CST
8_topic_area: Other; Mission-Enabling (internal agency support)
8_question_type: Consular Affairs
10_commercial_ai: None of the above.
11_purpose_benefits: CodeGen is an application designed for seamless integration with Large Language Models (LLMs) that aims to enhance the process that developers follow to build, test, and deploy software. Through use of API, it provides a secure suite of capabilities that fast-tracks developers during code generation, translation, analysis, optimization, and debugging.
12_outputs: Improved outcomes.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/9/2023
19_date_acq_dev_began: 8/22/2023
22_dev_method: Developed with both contracting and in-house resources.
33_agency_data: Application source code and documentation.
34_data_docs: Application source code and documentation. 
43_infra_provisioned: 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.
2_use_case_name: Violence Against Civilians Model
3_agency: Department of State
3_abr: STATE
4_bureau: CSO
8_topic_area: Other
8_question_type: Conflict Early Warning
11_purpose_benefits: A machine learning model that uses open-source political, social, and economic datasets to forecast mass civilian killings for the upcoming quarter and year for each country globally in order to inform conflict prevention. 
12_outputs: Forecasted quarterly and yearly estimates of indicators. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/23/2023
19_date_acq_dev_began: 1/23/2023
20_date_implemented: 1/23/2023
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
25_hisp_name: No
29_contains_pii: No
30_saop_review: No
34_data_docs: Documentation is complete
37_custom_code: No
43_infra_provisioned: Yes
45_compute_request: Other
47_timely_resources: Other