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 |
|---|---|---|---|---|---|---|
| Machine Learning for Avalanche Frequency Modeling DOI-0148 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Classifying CWD-Infected Elk Using Recurrent Neural Networks on GPS Movement Data DOI-0135 · USGS | DOI | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Deep?learning Integrations into NEIC Operations [2024 INV#WO0000000109496] DOI-0119 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| USGS Flow Photo Explorer [2024 INV#WO0000000109196] DOI-0117 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| Coastal Change Likelihood: Synthesizing change factors using supervised learning DOI-0115 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Predicting post-fire tree mortality DOI-0100 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Global food-and-water security-support analysis data (GFSAD) project [2024 INV#WO0000000107073] DOI-0095 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Frog vocalization recognition from digital recordings DOI-0092 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Patterns in the Landscape – Analyses of Cause and Effect DOI-0090 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Estimates of Habitat Suitability of Reed Canarygrass (Phalaris arundinacea) in Upper Mississippi River Floodplain Forest Understories DOI-0084 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Submersed Aquatic Vegetation Vulnerability Evaluation Application (SAVVEA) DOI-0081 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning in remote sensing-based wildfire and natural resource risk assessments DOI-0072 · USGS | DOI | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Telemetry Analysis Learning Algorithm (TALA) DOI-0070 · USGS | DOI | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| LANDFIRE DOI-0069 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Invasive Grass Mapping DOI-0068 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Evapotranspiration mapping and monitoring DOI-0067 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| National Land Cover Database (NLCD) [2024 INV#WO0000000107887] DOI-0066 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Microsoft eDiscovery Attorney-Client Privilege Detection DOI-0021 · SOL | DOI | Deployed | Not high-impact | Other | Classical / predictive ML | Purchased from vendor |
| Performance Modeling / Performance Forecasting DOI-0016 · SOL | DOI | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Effects of vehicle traffic on space use and road crossings of caribou in the Arctic [2024 INV#WO0000000110111] DOI-0007 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Sub-Asset Maintenance Model BEP-25 · Bureau of Engraving and Printing (BEP) | TREAS | Deployed | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Contract and in-house |
| Inventory Replenishment Forecast BEP-42 · Bureau of Engraving and Printing (BEP) | TREAS | Deployed | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Contract and in-house |
| IDV Model BEP-67 · Bureau of Engraving and Printing (BEP) | TREAS | Deployed | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Contract and in-house |
| Form 990N Machine Learning Model TREAS-125 · Internal Revenue Service (IRS) | TREAS | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Machine Translation TREAS-28 · Internal Revenue Service (IRS) | TREAS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Contract and in-house |
| Synthetic Data Engine TREAS-85 · Internal Revenue Service (IRS) | TREAS | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Purchased from vendor |
| Identity Verification TREAS-IRS-59 · Internal Revenue Service (IRS) | TREAS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Purchased from vendor |
| Integrate Word Processor AI for Digitalization TREAS-IRS-64 · Internal Revenue Service (IRS) | TREAS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Contract and in-house |
| Programmatic Access to LLMs via API TREASURY-OFR-002 · Office of Financial Research (OFR) | TREAS | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Insight R3095 · Chief Information Officer, Disability Information Systems | SSA | Deployed | Not high-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Intelligent Medical Language Analysis Generation (IMAGEN) R3096 · Chief Information Officer, Disability Information Systems | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Duplicate Identification Process R3097 · Chief Information Officer | SSA | Deployed | Not high-impact | Government Benefits Processing | Classical / predictive ML | Purchased from vendor |
| Modernized Development Worksheet (MDW) R3099 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | Not high-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Pre-Effectuation Review (PER) / Targeted Denial Review (TDR) Models R3100 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Continuing Disability Review Model R3101 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Social Security Income (SSI) Redetermination Model R3102 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Proactive Triage and Analysis of Hearings (PATH) R3103 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Quick Disability Determinations Model R3104 · Law and Policy, Disability Policy | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Purchased from vendor |
| PolicyNet Plain Language Search Engine R3108 · Chief Information Officer | SSA | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Benefit and Earnings Public Use File R3111 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Application Observability Support R3117 · Chief Information Officer, System Operations and Hardware En | SSA | Deployed | Not high-impact | Cybersecurity | Classical / predictive ML | Purchased from vendor |
| Automatic PSC Classification FTC-0001 · BCP/DCRO | FTC | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| Group Duplicate Complaints FTC-0002 · BCP/DCRO | FTC | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| Relativity Active Learning FTC-0008 · BC/BCP/OGC | FTC | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Purchased from vendor |
| AI-Augmented Declassification Review R3213 · A/SKS | STATE | Deployed | High-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| FA.gov RedactAid R3241 · F/FAO | STATE | Deployed | Not high-impact | International Affairs | Classical / predictive ML | Contract and in-house |
| Utility Invoices Data Extraction R3250 · MGT | STATE | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Machine Learning for geophysical data inversion DOE-195 · NETL - National Energy Technology Laboratory (FECM) | DOE | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Network Security and Analysis DOE-347 · IM-60 - IM Enterprise Operations and Shared Services (IM) | DOE | Deployed | Not high-impact | Cybersecurity | Classical / predictive ML | Purchased from vendor |
| Elucidating Genetic and Environmental Risk Factors for Antipsychotic-induced Metabolic Adverse Effects Using AI DOE-351 · PNNL - Pacific Northwest National Laboratory (SC43 OIM) | DOE | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |