AI use-case explorer
AI use cases
Search and filter every reported use case. Open a record for the agency's full description, governance answers and source provenance.
Columns (7)
| Use case | Agency | Stage | High-impact | Topic area | AI classification | Sourcing |
|---|---|---|---|---|---|---|
| Name Matching SEC-19 · Division of Examinations (EXAMS) | SEC | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Parsing of Plain Language Descriptions to Machine-Readable Notations SEC-20 · Division of Examinations (EXAMS) | SEC | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Using Machine Learning/Artificial Intelligence Techniques to Predict Entities With Certain Risk Characteristics SEC-24 · Division of Examinations (EXAMS) | SEC | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Identification of Potentially Manipulative Activity in Certain Accounts SEC-26 · Division of Examinations (EXAMS) | SEC | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Single Event Insider Trading Analysis SEC-34 · Division of Enforcement (ENF) | SEC | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| Tracing digital asset transactions and identifying entities controlling wallet addresses SEC-67 · Division of Enforcement (ENF), Division of Examinations (EXA | SEC | Deployed | Not high-impact | Other | Classical / predictive ML | Purchased from vendor |
| Improve efficiency of comment letter ingestion SEC-68 · Office of the Secretary (OS) | SEC | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Contract and in-house |
| Neural Networks for FHFA Modeling Analytics Platform (FMAP) FHFA-2 · DHMG | FHFA | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Phishing Email Identification FHFA-3 · OCOO | FHFA | Deployed | Not high-impact | Cybersecurity | Classical / predictive ML | Purchased from vendor |
| Security and network monitoring using Cisco Identify Services engine FHFA-10 · OCIO | FHFA | Deployed | Not high-impact | Cybersecurity | Classical / predictive ML | Purchased from vendor |
| Virtual Desktop using Citrix FHFA-11 · OCIO | FHFA | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Purchased from vendor |
| Analytics, Indexing, and Anomaly Detection for Data Management on SQL Server FHFA-12 · OCIO | FHFA | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Purchased from vendor |
| Workflow automation and predictive analytics using ServiceNow FHFA-13 · OCIO | FHFA | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Purchased from vendor |
| Security and Compliance in file sharing and collaboration using Kiteworks FHFA-14 · OCIO | FHFA | Deployed | Not high-impact | Cybersecurity | Classical / predictive ML | Purchased from vendor |
| Network monitoring, anomaly detection through Whats Up Gold and Flowmon integration FHFA-15 · OCIO | FHFA | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Purchased from vendor |
| AEGIS: Autonomous Exploration for Gathering Increased Science NASA-214 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| CLASP Coverage Planning & Scheduling NASA-221 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Enhanced AutoNav for Perseverance Rover on Mars NASA-225 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| MLNav (Machine Learning Navigation) NASA-234 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Presumed high-impact, determined not | Science | Classical / predictive ML | Developed in-house |
| Perseverance Rover on Mars - Terrain Relative Navigation NASA-237 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| SensorWeb: Volcano, Flood, Wildfire, and others. NASA-451 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Global, Seasonal Mars Frost Maps NASA-708 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Purchase Card Management System (PCMS) NASA-715 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| New Technology and Software Reporting (NTR) NASA-716 · JPL: Jet Propulsion Laboratory | NASA | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Near-real-time aerosol retrievals from OMPS Limb Profiler measurements NASA-848 · GSFC: Goddard Space Flight Center | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Retrieving stratospheric water vapor from OMPS Limb Profiler measurements NASA-849 · GSFC: Goddard Space Flight Center | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Application of ML to Detection of Anomalies in Spacecraft Health and Status Data NASA-854 · GSFC: Goddard Space Flight Center | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Dirty Vacuum rated 6-Axis Robotic Arm Toolpathing NASA-928 · MSFC: Marshall Space Flight Center | NASA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Counterparty Risk Anomaly Detection HUD-2024-001 · Ginnie Mae | HUD | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Subledger Data Quality Machine Learning HUD-2024-002 · Ginnie Mae | HUD | Deployed | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Contract and in-house |
| Economic Trend Modeling FRB-0001 · Division of Monetary Affairs | FRB | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| PDF Optical Character Recognition (Text) FRB-0003 · Division of Information Technology | FRB | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Commercial Real Estate Index FRB-0009 · Division of Supervision and Regulation | FRB | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Regulatory Data Analysis FRB-0028 · Division of Supervision and Regulation | FRB | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Risk Rating Model – Community Banks FRB-0049 · Division of Supervision and Regulation | FRB | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| AHRQ Search R0622 · HHS/AHRQ/CQuIPS | HHS | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Contract and in-house |
| Short-term Forecasting of Severe Outcomes for Seasonal and Epidemic Pathogens R0655 · HHS/CDC | HHS | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Contract and in-house |
| Malaria parasites DNA barcode geography classification R0658 · HHS/CDC | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Genetic distance computation method for comparing complex multi-locus parasite (Cyclospora) genotypes R0673 · HHS/CDC | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| NIOSH Industry and Occupation Computerized Coding System (NIOCCS) R0676 · HHS/CDC | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Nowcasting Injury Trends R0677 · HHS/CDC | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Risk Assessment Module (RAM) for the National Diabetes Prevention Program (National DPP) Operations Center. R0678 · HHS/CDC | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage R0680 · HHS/CDC | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Nowcasting Burden and Infection Trends for Seasonal and Epidemic Pathogens R0688 · HHS/CDC | HHS | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Contract and in-house |
| Machine Learning with Premier Healthcare Data to inform predictive modeling of antibiotic use R0695 · HHS/CDC | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Machine Learning Techniques for Early Detection and Situational Awareness of Rabies Outbreaks R0696 · HHS/CDC | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Lineage Assignment by Extended Learning (LABEL) R0717 · HHS/CDC | HHS | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| AI-assisted comment triaging tool R0740 · HHS/CMS/CM | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Independent Dispute Resolution (IDR) Eligibility Rules Engine R0757 · HHS/CMS/CCIIO | HHS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Purchased from vendor |
| Docketscope Public Comment Processing R0762 · HHS/CMS/CM | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Purchased from vendor |
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