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 |
| 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 |
| 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 |
| 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 |
| 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 |
| AI-assisted comment triaging tool R0740 · HHS/CMS/CM | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Renamed: 356H Machine Learning (ML) Facility Supply Chain Role Classification
Previously: 356H ML Facility Supply Chain Role Classification R0816 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Renamed: Risk-based FAR Review & Decision Support
Previously: Field Alert Reports (FAR) Prioritization Model R0817 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Resource Capacity Planning R0832 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Warp Intelligent Learning Engine (WILEE) R0864 · HHS/FDA/HFP | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Product Label and Text Extraction System (PLATES) R0867 · HHS/FDA/HFP | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Machine Learning as a Service: Translate and extract text from images using AI R0876 · HHS/FDA/ODT | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Machine Learning as a Service: Extract data from product labels, business forms, and image files R0877 · HHS/FDA/ODT | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Filer Evaluation prioritization using risk-based decision Machine Learning approach R0878 · HHS/FDA/OII | HHS | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Contract and in-house |
| ClinicalTrials.gov Protocol Registration and Results System Review Assistant R0909 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| DAIT AIDS-Related Research Solution R0912 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Federal IT Acquisition Reform Act (FITARA) Tool R0916 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Internal Referral Module (IRM) R0919 · HHS/NIH | HHS | Deployed | Presumed high-impact, determined not | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Research Area Tracking Tool R0972 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| TB DEPOT (Tuberculosis Data Exploration Portal) R0974 · HHS/NIH | HHS | Deployed | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| GrantSolutions Recipient Risk Tool R1033 · HHS/ASFR/OG | HHS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Contract and in-house |
| GrantSolutions Non?Competing Continuation Approval Tool R1034 · HHS/ASFR/OG | HHS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Contract and in-house |
| Automatically-Scored Writing Assessment (AWA) FDIC – 32 · Division of Administration | FDIC | Deployed | High-impact | Human Resources | Classical / predictive ML | Contract and in-house |
| Expenditure Classification
Autocoder DOL-21 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Worker PLUS Microsimulation Program DOL-27 · CEO - WB | DOL | Deployed | Not high-impact | Human Resources | Classical / predictive ML | Contract and in-house |
| Ecosystem Management Decision Support System (EMDS) USDA-014 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Contract and in-house |
| Nutrition Education & Local Access Dashboard USDA-039 · Food, Nutrition, and Consumer Services | USDA | Deployed | Not high-impact | Government Benefits Processing | Classical / predictive ML | Contract and in-house |
| Rangeland Analysis Platform USDA-046 · Research, Education and Economics; Farm Production and Conse | USDA | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Contract and in-house |
| The Big Data, Mapping, and Analytics Platform (BIGMAP) Project USDA-079 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| AI for regional forest mapping and monitoring USDA-089 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Contract and in-house |
| Azure Data Factory DOJ-0006 · Department of Justice / ATF | DOJ | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Contract and in-house |
| Azure Zen 2 Storage DOJ-0007 · Department of Justice / ATF | DOJ | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Contract and in-house |
| Data & Analytics: Chemistry Instrument Library DOJ-0081 · Department of Justice / DEA | DOJ | Deployed | Not high-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| TIPS DOJ-0136 · Department of Justice / FBI | DOJ | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Contract and in-house |
| JMIS: Route Optimizer DOJ-0228 · Department of Justice / USMS | DOJ | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Fingerprint (Friction Ridge) Optical Character Recognition (OCR) DOJ-0296 · Department of Justice / DEA | DOJ | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| ERNIE DHS-315 · CBP | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| PreCheck Touchless Identity Solution DHS-345 · TSA | DHS | Deployed | High-impact | Transportation | Classical / predictive ML | Contract and in-house |
| Text Analytics Data Science Sentence Similarity Model DHS-130 · USCIS | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| Verification Match Model DHS-2384 · USCIS | DHS | Deployed | High-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Person-Centric Identity Services Deduplication Model DHS-55 · USCIS | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| The Advanced Trade Analytics Platform (ATAP) DHS-101 · CBP | DHS | Deployed | Presumed high-impact, determined not | International Affairs | Classical / predictive ML | Contract and in-house |
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