GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Automated Damage Assessments
3_agency: Department of State
3_abr: STATE
4_bureau: CSO
8_topic_area: Diplomacy & Trade
11_purpose_benefits: The Conflict Observatory cooperative agreement used AI and machine learning on moderate and high-resolution commercial satellite imagery to create automated damage assessments. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 5/17/2022
19_date_acq_dev_began: 5/17/2022
20_date_implemented: 11/15/2022
21_date_retired: 8/31/2023
2_use_case_name: Automated Burning Detection 
3_agency: Department of State
3_abr: STATE
4_bureau: CSO
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Daily scans of moderate resolution commercial satellite imagery to identify anomalies using the near-infrared band. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 1/31/2021
19_date_acq_dev_began: 1/31/2021
20_date_implemented: 2/1/2022
21_date_retired: 7/31/2023
2_use_case_name: Senturion Alpha
3_agency: Department of State
3_abr: STATE
4_bureau: CSO
8_topic_area: Other
8_question_type: Conflict Early Warning and Negotations
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: A stakeholder/influence-driven model that identifies where key decision makers fall on an issue spectrum and who influences whom.  The simulation analyzes the political dynamics within contexts and estimates how the policy positions of competing interests will evolve over time.
12_outputs: A simulation.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Mass Mobilization 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 forecasting model that uses open-source political, social, and economic datasets to predict mass mobilizations (protests and riots) for each country globally. 
12_outputs: Forecasted yearly estimates.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 1/1/2024
20_date_implemented: 1/1/2024
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
2_use_case_name: Apptio
3_agency: Department of State
3_abr: STATE
4_bureau: DT
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Working Capital Fund (IRM/WCF) used Apptio to bill bureaus for consolidated services run from the WCF. Cost models were built in Apptio so bureaus can budget for the service costs in future Fiscal Years (FYs). Apptio had the capability to extrapolate future values using several available formulas. This project is retired.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/1/2023
21_date_retired: 9/29/2024
2_use_case_name: DT Data Analytics and Assessment (DAA) AI Use Case ITCP data harvest
3_agency: Department of State
3_abr: STATE
4_bureau: DT
8_topic_area: Other
8_question_type: Enterprise Services Directorate (ES). The Data Analytics Unit (DAU) within Diplomatic Technologies Enterprise Services provides stakeholders across the Agency with access to data, empowering them to make informed decisions. Enhancing our AI/ML capabilities within core data platforms would significan
10_commercial_ai: None of the above.
11_purpose_benefits: To allow DT DAA team to parse unstructured text from key DT documents to build structured data indicating compliance to DT data governance and DT FAM related policies.
12_outputs: Plan to have structured data.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: ServiceNow AI-Powered Virtual Agent (Chatbot)
3_agency: Department of State
3_abr: STATE
4_bureau: DT/BMP/ITA/SSM
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: DT (formerly IRM) BMP Systems planned to incorporate ServiceNow's Virtual Agent into existing applications to connect users with support and data requests. The Artificial Intelligence (AI) was provided by ServiceNow as part of their Platform as a Service (PaaS). This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 7/1/2023
19_date_acq_dev_began: 10/1/2022
20_date_implemented: 9/30/2023
21_date_retired: 6/29/2024
2_use_case_name: POA&M Orchestration
3_agency: Department of State
3_abr: STATE
4_bureau: DT/DCIO/CO/A&A
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: This planned AI Use Case involves the orchestration of automatically opening and closing of Plan of Action and Milestones (POA&Ms) based upon log and vulnerability data specific to each Federal Information Security Modernization Act (FISMA) system.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: NLP for Foreign Assistance Appropriations Analysis
3_agency: Department of State
3_abr: STATE
4_bureau: F
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Natural Language Processing (NLP) application for F/RA to streamline the extraction of earmarks and directives from the annual appropriations bill. Before NLP this was an entirely manual process.
12_outputs: Extracted data.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
25_hisp_name: No
43_infra_provisioned: No
43_question_type: Not required
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: ForeignAssistance.gov Processing for Mismatched Data
3_agency: Department of State
3_abr: STATE
4_bureau: F
8_topic_area: Other
8_question_type: Foreign Assistance financial data
11_purpose_benefits: Created a custom Natural Language Processing (NLP) model to recommend information for tagging and identify mismatched data. 
12_outputs: Potential mismatches and suggested tags.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 7/24/2023
20_date_implemented: 7/6/2023
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
25_hisp_name: No
33_agency_data: Foreign Assistance Obligations and Disbursements.
34_data_docs: Yes
43_infra_provisioned: No
43_question_type: Not required
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: Creating Persistent Virtual Reality Personas for dynamic training
3_agency: Department of State
3_abr: STATE
4_bureau: FSI/EX
8_topic_area: Education & Workforce
11_purpose_benefits: The EdTech Innovation Lab team plans to develop a dynamic integration between multiple AI tools to create stored/persistent "personas" that can be interacted with during training-related virtual reality (VR) exercises.
12_outputs: Plan to have persistent personas.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: FSI Enterprise Operations - Gaming and Simulations
3_agency: Department of State
3_abr: STATE
4_bureau: FSI/EX
8_topic_area: Education & Workforce
11_purpose_benefits: In FY 2024, the bureau began a pilot program to better accommodate the need for experiential learning through gaming and simulations.  Simulations can involve complicated scenarios in several different target languages. 
12_outputs: Plan to have simulations.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: FSI Continuous Learning Solutions
3_agency: Department of State
3_abr: STATE
4_bureau: FSI/EX
8_topic_area: Education & Workforce
11_purpose_benefits: FSI plans to sustain our AI action plan approved by the Front Office to promote content generation, analytical tasks, and increased overall efficiency and productivity around content development.
12_outputs: Improved outcomes.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: Enhancing Training Effectiveness in FSILearn Using AI
3_agency: Department of State
3_abr: STATE
4_bureau: FSI/EX/EDS
8_topic_area: Education & Workforce
11_purpose_benefits: Plan to use AI on the Cornerstone Learning Management System to enhance employee learning experiences by analyzing key data related to learning behaviors, such as course completions, quiz scores, and engagement levels. The AI uses this information to tailor learning paths.
12_outputs: Plan to have tailored learning paths.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: J Reports Data Collection & Management Tool (DCT)
3_agency: Department of State
3_abr: STATE
4_bureau: J
8_topic_area: Diplomacy & Trade; Mission-Enabling (internal agency support)
11_purpose_benefits: The DCT is an AI-driven research assistant designed to increase the quality and maintain the integrity of select Congressional reports while reducing the labor hours it takes to generate them. The tool automates the summarization, translation, and categorization of user-submitted content into a searchable database, replacing the manual tracking and organizing of research.  Users can upload materials throughout the year and easily retrieve content during drafting season. 
12_outputs: The DCT provides AI-generated summaries and translations of user-submitted research.  It also suggests categories to organize the research according to instructions. The DCT uses optical character recognition (OCR) to digitize images and documents (e.g., PDFs).
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/1/2023
19_date_acq_dev_began: 6/20/2023
20_date_implemented: 11/29/2023
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
33_agency_data: DCT Evaluation Data: An internal research dataset of user-submitted articles, reviewed and labeled by subject matter experts to facilitate model evaluation.
34_data_docs: Documentation is widely available
37_custom_code: Yes
40_has_ato: Data.State-SBU
41_system_name: Data.State-SBU
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: TIP Report Research Translation
3_agency: Department of State
3_abr: STATE
4_bureau: J/TIP
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The TIP Report Translation AI system provides informal, unofficial translations for materials related to the annual Trafficking in Persons report.  This capability saves staff time and allows researchers and drafters to focus on critical report needs, such as identifying and resolving data gaps. 
12_outputs: Informal, unofficial translations of PDFs and Microsoft word documents. Each document includes a watermark denoting that the AI translation is unofficial. Each document must be verified, reviewed, and edited by a human.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2023
19_date_acq_dev_began: 6/20/2023
20_date_implemented: 9/1/2023
2_use_case_name: FOIA 360 AI Matching Tool
3_agency: Department of State
3_abr: STATE
4_bureau: M/SS/CFA
8_topic_area: Other
8_question_type: Government Transparency
11_purpose_benefits: This system aims to make the Department of State's FOIA process better by spotting similar requests and documents to cut down on duplication. It also helps find patterns in both current and new requests to speed up response times for the public.
12_outputs: Improved outcomes.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 3/2/2023
19_date_acq_dev_began: 6/5/2023
20_date_implemented: 4/1/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
25_hisp_name: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
33_agency_data: FOIA request text from FOIA Express and documents captured from the Department's Virtual FOIA Reading Room are used for training and testing
34_data_docs: Documentation is complete
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: ElasticSearch
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 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: StateChat
3_agency: Department of State
3_abr: STATE
4_bureau: M/SS/CFA
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: StateChat is the Department's enterprise Generative AI-powered chatbot.
12_outputs: StateChat is a chatbot, its outputs are textual in a chat interface.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 2/1/2024
20_date_implemented: 3/1/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
25_hisp_name: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: The model is not trained or fine-tuned with agency data; it is provided various types of agency data as context.  
34_data_docs: Documentation is widely available
37_custom_code: Yes
40_has_ato: Yes
41_system_name: StateChat
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 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: Crisis Campaign Cable Analytics
3_agency: Department of State
3_abr: STATE
4_bureau: M/SS/CFA
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Used optical character recognition and Natural Language Processing (NLP) on Department cables in order to evaluate gaps and trends in crisis training and bolster post preparedness for crisis events.  This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 1/16/2023
19_date_acq_dev_began: 4/3/2023
20_date_implemented: 5/15/2023
21_date_retired: 5/31/2023
2_use_case_name: NLP to pull key information from unstructured text
3_agency: Department of State
3_abr: STATE
4_bureau: PM
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Natural language processing (NLP) to pull key information from unstructured text.  Use NLP to extract information such as country names and agreement dates from dozens of pages of unstructured PDF document.
12_outputs: Dataset with key information from unstructured data.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/1/2022
19_date_acq_dev_began: 4/15/2022
20_date_implemented: 9/30/2022
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
42_dev_tools_wait: Less than 6 months
47_timely_resources: Other
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.
2_use_case_name: ECA Program Management and Outreach - Summarization
3_agency: Department of State
3_abr: STATE
4_bureau: R/ECA/EX/IT
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Use approved Large Language Models to create artifacts for leadership as well as visiting legateship utilizing relevant and accurate datasets.
12_outputs: Plan to have draft summaries that will be reviewed by humans.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: Storyzy
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Other
8_question_type: Synthetic media detection
10_commercial_ai: None of the above.
11_purpose_benefits: Improve detected use of synthetic content.- Synthetic content is computer-generated data that mimics real-world data.
12_outputs: Improved outcomes.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 7/17/2023
20_date_implemented: 12/1/2023
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
33_agency_data: Open source data from social media.
34_data_docs: No
37_custom_code: 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.
2_use_case_name: Optical Character Recognition - Text Extraction
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Extracted text from images using standard python libraries; inputs were websites to collect data. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 9/21/2021
19_date_acq_dev_began: 9/21/2021
20_date_implemented: 12/21/2021
21_date_retired: 6/30/2023
2_use_case_name: Topic Modeling
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Cluster text into themes based on frequency of used words in documents; was applied to digital media articles as well as social media posts; performed using available Python libraries.  This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/30/2023
2_use_case_name: Forecasting
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Using statistical models, projecting expected outcomes into the future; this was applied to COVID cases as well as violent events in relation to tweets. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/30/2023
2_use_case_name: Deepfake Detector
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Deep learning model that took in an image containing a person's face and classifies the image as either being real (contains a real person's face) or fake (synthetically generated face, a deepfake often created using Generative Adversarial Networks). This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 6/21/2021
19_date_acq_dev_began: 6/21/2021
20_date_implemented: 9/21/2021
21_date_retired: 6/30/2023
2_use_case_name: SentiBERTIQ
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Used deep contextual AI of text to identify and extract subjective information within the source material. This sentiment model was trained by fine-tuning a multilingual, BERT model leveraging word embeddings across 2.2 million labeled tweets spanning English, Spanish, Arabic, and traditional Chinese. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/30/2023
2_use_case_name: TOPIQ
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Automatically categorized text into topics for analyst review and interpretation, using Latent Dirichlet Allocation (LDA), to extract topics from documents and then assigned the probability that each document belonged to a topic. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/30/2023
2_use_case_name: Text Similarity
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Identified different texts that were identical or nearly identical by calculating cosine similarity between each text. Texts were then grouped if they share high cosine similarity and then available for analysts to review further. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/30/2023
2_use_case_name: Image Clustering
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Used a pretrained deep learning model to generate image embeddings, then used hierarchical clustering to identify similar images. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/30/2023
2_use_case_name: Louvain Community Detection
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Took in a social network and clusters nodes together into "communities" (i.e., similar nodes are grouped together). This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/30/2023
2_use_case_name: Fast Text Word Builder
3_agency: Department of State
3_abr: STATE
4_bureau: R/GEC
8_topic_area: Diplomacy & Trade
11_purpose_benefits: Fast Text was an AI approach to identifying similar terms and phrases based on a root word. This supported A&R's capability to build robust search queries for data collection. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 3/15/2023
19_date_acq_dev_began: 4/1/2023
20_date_implemented: 4/1/2023
21_date_retired: 6/30/2023
2_use_case_name: Digital Media Analytics Platform
3_agency: Department of State
3_abr: STATE
4_bureau: R/GPA/RA
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Using open-source neural machine translation models to translate global media articles and Department and public foreign social media posts into English. 
12_outputs: Trend analysis
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/1/2022
19_date_acq_dev_began: 12/1/2022
20_date_implemented: 1/1/2024
22_dev_method: Developed with both contracting and in-house resources.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
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.