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 |
|---|---|---|---|---|---|---|
| Vendor Risk Analytics TREAS-IRS-9 · Internal Revenue Service (IRS) | TREAS | Pilot | Presumed high-impact, determined not | Procurement and Financial Management | Classical / predictive ML | Developed in-house |
| NCCBR Data Validation Pilot TREASURY-OFR-003 · Office of Financial Research (OFR) | TREAS | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| MI8 Collimators Surogate Model DOE-453 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning for Linac Improved Performance DOE-60 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| In-pixel AI for future tracking detectors DOE-66 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| SONIC: AI acceleration as a service DOE-67 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Advanced Long Term Environmental Monitoring Systems (ALTEMIS) DOE-714 · SRS - SRNL - Savannah River Site - Savannah River National L | DOE | Pilot | High-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Denoising Diffusion to Accelerate Detector Simulation DOE-716 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE DOE-73 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model NASA-13 · ARC: Ames Research Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning for prediction and factor analysis of laser forming parameters NASA-932 · MSFC: Marshall Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| RDUST Regolith Dust Universal System Tracker NASA-935 · MSFC: Marshall Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Lung Cancer Prediction Model VA-24-3898 · VHA: Veterans Health Administration | VA | Pilot | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Screen for Esophageal Adenocarcinoma VA-25-5375 · VHA: Veterans Health Administration | VA | Pilot | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Computer-Assisted Review:
ORS Autocoder DOL-41 · BLS | DOL | Pre-deployment | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Forest disease detection and screening USDA-071 · Natural Resources and Environment | USDA | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Markov random fields for mixed forests USDA-088 · Natural Resources and Environment | USDA | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Audit Green Book Classification SBA-3 · OCFO: Office of the Chief Financial Officer | SBA | Pre-deployment | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Internal OFO Chatbot Recommender SBA-4 · OFO: Office of Field Operations | SBA | Pre-deployment | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| 7(a) Purchases ML Model SBA-7 · OCA: Office of Capital Access | SBA | Pre-deployment | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Managing curb allocation in cities DOE-357 · PNNL - Pacific Northwest National Laboratory (SC43 OIM) | DOE | Pre-deployment | Not high-impact | Transportation | Classical / predictive ML | Developed in-house |
| Surrogate models for probabilistic Bayesian inference DOE-361 · PNNL - Pacific Northwest National Laboratory (SC43 OIM) | DOE | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Crickets DOE-723 · SRS - ESB - Savannah River Site - Enterprise System Boundary | DOE | Pre-deployment | Not high-impact | Cybersecurity | Classical / predictive ML | Developed in-house |
| Harmful Algal Bloom prediction and detection system for Williams Fork Reservoir [2024 INV#WO0000000184339] DOI-0244 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning algorithm for stream velocity prediction [2024 INV#WO0000000109319] DOI-0226 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Wildlife species recognition and distance from camera estimation [2024 INV#WO0000000109245] DOI-0220 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| SAMPLE Toolbox [2024 INV#WO0000000109123] DOI-0206 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Data-driven approaches to filling missing time-series data within the San Francisco Bay-Delta [2024 INV#WO0000000107683; INV#WO0000000107488] DOI-0174 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning based shoreline detection and sea ice dynamics using coastal cameras [2024 INV#WO0000000108008] DOI-0166 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Tsunami Hazard Analysis [2024 INV#WO0000000108470] DOI-0165 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Determining the resource potential of critical minerals in seafloor massive sulfide deposits [2024 INV#WO0000000109311] DOI-0163 · USGS | DOI | Pre-deployment | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Improved earthquake detection for research studies [2024 INV#WO0000000108499] DOI-0147 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Improved Processing and Analysis of Test and Operating Data from Rotating Machines [2024 INV#DOI-62] DOI-0041 · BOR | DOI | Pre-deployment | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Using advanced computing techniques for image-based monitoring [2024 INV#WO0000000109674] DOI-0034 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Water Use Model Development [2024 INV#WO0000000109669] DOI-0030 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Report Assistant DOE-610 · SRS - ESB - Savannah River Site - Enterprise System Boundary | DOE | Pre-deployment | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Machine Learning to support Operational Efficiency DOE-726 · SRS - ESB - Savannah River Site - Enterprise System Boundary | DOE | Pre-deployment | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| GovCon Match SBA-17 · OCIO: Office of the Chief Information Officer | SBA | Retired | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
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