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 |
|---|---|---|---|---|---|---|
| Neural Networks for FHFA Modeling Analytics Platform (FMAP) FHFA-2 · DHMG | FHFA | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| 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 |
| Economic Trend Modeling FRB-0001 · Division of Monetary Affairs | FRB | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Manufacturer Sentiment Analysis FRB-0016 · Division of Research and Statistics | FRB | Deployed | Not high-impact | Other | Natural language processing | Developed in-house |
| Supply Chain Estimations FRB-0017 · Division of Research and Statistics | FRB | Deployed | Not high-impact | Other | Natural language processing | Developed 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 |
| 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 |
| MedWatch Dashboard R0820 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Health and Medical | Natural language processing | Contract and in-house |
| Resource Capacity Planning R0832 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Supply Chain Resilience Program, Office of Supply Chain Resilience (OSCR) - Foresight R0833 · HHS/FDA/CDRH | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Operational Water Supply Forecasting for Western US Rivers USDA-027 · Farm Production and Conservation | USDA | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Bloomberg Government DOJ-0008 · Department of Justice / ATF | DOJ | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Purchased from vendor |
| Insider Threat Management and User Activity Monitoring DOJ-0119 · Department of Justice / EOUSA | DOJ | Deployed | High-impact | Law Enforcement | Generative AI | Purchased from vendor |
| Illicit Trade DHS-2391 · CBP | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Trade Entity Risk Model DHS-95 · CBP | DHS | Deployed | Presumed high-impact, determined not | Law Enforcement | Classical / predictive ML | Developed in-house |
| Cyber Threat Analysis (Recorded Future) DHS-399 · CBP | DHS | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Purchased from vendor |
| Critical Infrastructure Network Anomaly Detection DHS-106 · CISA | DHS | Deployed | Not high-impact | Cybersecurity | Classical / predictive ML | Developed in-house |
| Individual Assistance (IA) Predictive Models for Program Quantities DHS-2722 · FEMA | DHS | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Airport Throughput Predictive Model DHS-2432 · TSA | DHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Supervisory Stress Testing NCUA_ONES-02 · NCUA|ONES | NCUA | Deployed | High-impact | Procurement and Financial Management | Classical / predictive ML | Developed in-house |
| Delineating sub-surface drainage using satellite imagery [2024 INV#WO0000000109525] DOI-0230 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Predicting post-fire tree mortality DOI-0100 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Performance Modeling / Performance Forecasting DOI-0016 · SOL | DOI | Deployed | Not high-impact | Administrative Functions | 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 |
| 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 |
| AI used for predictive modeling and real time control of traffic systems DOE-353 · PNNL - Pacific Northwest National Laboratory (SC43 OIM) | DOE | Deployed | Not high-impact | Transportation | Reinforcement learning | Contract and in-house |
| Stratification Tool for Opioid Risk Mitigation (STORM) VA-24-4152 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| REACH VET Suicide Risk Prediction and Recovery Engagement VA-24-4234 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Elastic Machine Learning Threat Detection (Phase 1) R1179 | GSA | Deployed | Not reported | Cybersecurity | Classical / predictive ML | Not reported |
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