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 |
|---|---|---|---|---|---|---|
| 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 |
| Census of Agriculture Propensity Scores via Machne Learning USDA-035 · Research, Education and Economics | USDA | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Survey Outlier Detection Model USDA-041 · Research, Education and Economics | USDA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Predictive Cropland Data Layer USDA-047 · Research, Education and Economics | USDA | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| DISTRIB-II: Habitat Suitability of Eastern United States Tree USDA-051 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| US Poultry and Operations Dataset USDA-064 · Marketing and Regulatory Programs | USDA | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| County-Level Remotely-Sensed Corn and Soybean Yield Estimation USDA-067 · Research, Education and Economics | USDA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| BirdNET to detect bird vocalizations for research and species monitoring USDA-080 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Predictive flood modeling USDA-082 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Transportation | Classical / predictive ML | Developed in-house |
| Esri ArcGIS Pro Deep Learning Modules USDA-091 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Policy Analysis USDA-138 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| LANDFIRE USDA-167 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| ChatBot DOJ-0046 · Department of Justice / COPS | DOJ | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Prisoner Assessment Tool Targeting Estimated Risk and Needs (PATTERN) DOJ-0160 · Department of Justice / FBOP | DOJ | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Static-99 Sex Offender Data System (SODS) DOJ-0163 · Department of Justice / FBOP | DOJ | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| JMIS: JARS DOJ-0227 · Department of Justice / USMS | DOJ | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Entity Resolution DOJ-0299 · Department of Justice / FBI | DOJ | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Passenger Security Assessment Model DHS-2389 · CBP | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Cargo Security Assessment Model DHS-2390 · CBP | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Illicit Trade DHS-2391 · CBP | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Traveler Entity Resolution DHS-80 · CBP | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Hurricane Score DHS-2408 · ICE | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Passport Anomaly Model DHS-81 · CBP | DHS | Deployed | Presumed high-impact, determined not | Law Enforcement | Classical / predictive ML | Developed in-house |
| Agriculture Commodity Model (AGC) DHS-86 · CBP | DHS | Deployed | Presumed high-impact, determined not | 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 |
| Empty Container Detection Model (Cargo Insights Team) DHS-68 · CBP | DHS | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Cargo Classification Tool DHS-94 · CBP | DHS | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| Critical Infrastructure Network Anomaly Detection DHS-106 · CISA | DHS | Deployed | Not high-impact | Cybersecurity | Classical / predictive ML | Developed in-house |
| Public Assistance Workload Projections DHS-2720 · FEMA | DHS | Deployed | Not high-impact | Emergency Management | 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 |
| AI Assisted Compromise Email Detector (AACED) DHS-2424 · ICE | DHS | Deployed | Not high-impact | Cybersecurity | 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 |
| Adaptive Risk Model for Inspected Small Passenger Vessels DHS-178 · USCG | DHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Streamflow Duration Assessment Modeling 25 · OW | EPA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Microsoft Power Automate (custom document processing model) 35 · OFA | EPA | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Living Literature Review: Semi-automated Literature Screening 76 · OAR | EPA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Using machine learning methods to analyze drivers of water quality 77 · AO | EPA | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Machine Learning Data Validation NCUA_E&I-01 · NCUA|E&I | NCUA | Deployed | Not high-impact | Procurement and Financial Management | 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 |
| Automated Walrus Haulout Monitoring [2024 INV#WO0000000110052] DOI-0247 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| PAWSC Ecotoxicology PFAS Machine Learning [2024 INV#WO0000000112908] DOI-0238 · USGS | DOI | Deployed | Not high-impact | Science | 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 |
| Oil Spill Response for Ice-Covered Rivers [2024 INV#WO0000000109142] DOI-0208 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Pacific Northwest Stream Flow Permanence [2024 INV#WO0000000109137] DOI-0207 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Climate Futures for Lizards and Snakes in Western North America [2024 INV#WO0000000109092] DOI-0201 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Using Machine Learning in USGS StreamStats to make suspended sediment and bedload predictions [2024 INV#WO0000000107977] DOI-0176 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Rangeland Condition Monitoring Assessment and Projection (RCMAP) [2024 INV#WO0000000107126] DOI-0171 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| use of random forest for species distribution modeling DOI-0161 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| PRObability of Streamflow PERmanence (PROSPER models) [2024 INV#WO0000000109074] DOI-0156 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning for automatic fracture mapping and rock identification [2024 INV#WO0000000109499] DOI-0155 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |