GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Computationally efficient emulation of spheroidal elastic deformation sources using machine learning [2024 INV#WO0000000109302]
Agency: Department of the Interior - USGS
Problem addressed: analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software
Purpose and benefits: we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity
Topic area: Science
Development stage: Pilot
First used: 2023-01-02
US federal AI use case: Quantifying the effects of land-use change and bioenergy crop production on pollinators, wildlife, and ecosystem services in the Northern Great Plains [2024 INV#WO0000000109305]
Agency: Department of the Interior - USGS
Problem addressed: Quantifying the effects of land-use change and bioenergy crop production on pollinators, wildlife, and ecosystem services in the Northern Great Plains
Purpose and benefits: Ability to Quantifying the effects of land-use change and bioenergy crop production on pollinators, wildlife, and ecosystem services in the Northern Great Plains
Topic area: Science
Development stage: Pilot
First used: 2018-10-01
US federal AI use case: Oceanographic, coastal, and geomorphic change analysis: data generation, QC/QA, and data management [2024 INV#WO0000000109310]
Agency: Department of the Interior - USGS
Problem addressed: Machine learning to quantify coastal/marine change across broad scales.  QC/QA processes in place to assess data robustness.
Purpose and benefits: Verified data will be used by USGS projects for forecasting trends (ie, shorelines, role of permafrost) in a variety of coastal/marine settings for US coasts.
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Machine learning for tsunami source zones [2024 INV#WO0000000109313]
Agency: Department of the Interior - USGS
Problem addressed: State of the art tsunami hazard analysis for coastal communities and infrastructure is computationally demanding.
Purpose and benefits: computational efficiency
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Automated otolith aging using image processing [2024 INV#WO0000000109315]
Agency: Department of the Interior - USGS
Problem addressed: Fisheries managers and researchers often need to know the age of fish for population estimates, stock assessment, and similar projects. Fish otoliths (an ear bone) often accumulated rings annual (similar to tress).
Purpose and benefits: automate this process to see if we can reduce variability across individual agers and automate the aging process of counting otolith rings possibly saving time
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Machine Learning algorithm for stream velocity prediction [2024 INV#WO0000000109319]
Agency: Department of the Interior - USGS
Problem addressed: time-of-travel web-based application that will allow users to estimate travel times in a spill response scenario with greater accuracy
Purpose and benefits: more accurate predictions of travel times in a spill response scenario
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Population and critical habitat modeling of overwintering monarch butterflies [2024 INV#WO0000000109410]
Agency: Department of the Interior - USGS
Problem addressed: Monarch butterflies in the western United States overwinter at very specific locations across coastal California. As monarch population decline it become important to identify the characteristics of what makes an overwintering grove a suitable habitat.
Purpose and benefits: Understanding the land cover and climatic factors that influence site selection by monarch can aid land managers in both making decisions to support existing critical habitat, and identify previously unknown locations where monarchs overwinter
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Reinforcement Learning for Helmholtz Coil Operation and Simulation [2024 INV#WO0000000109497]
Agency: Department of the Interior - USGS
Problem addressed: optimize performance of its magnetic observatories
Purpose and benefits: reinforcement learning (RL) can significantly aid in the operation of a Helmholtz coil by optimizing its performance in generating uniform magnetic fields
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Vegetation mapping on the Hawaiian island of Lanai [2024 INV#WO0000000109501]
Agency: Department of the Interior - USGS
Problem addressed: accurately classify plant species across the Hawaiian island of Lanai, producing detailed maps that can support conservation planning and monitoring of both native and invasive species
Purpose and benefits: accurately classify plant species
Topic area: Science
Development stage: Pilot
First used: 2024-03-07
US federal AI use case: Delineating sub-surface drainage using satellite imagery [2024 INV#WO0000000109525]
Agency: Department of the Interior - USGS
Problem addressed: Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response.
Purpose and benefits: a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management
Topic area: Science
Development stage: Deployed
First used: 2024-11-26
US federal AI use case: Mapping sagebrush from drones to satellites [2024 INV#WO0000000109568]
Agency: Department of the Interior - USGS
Problem addressed: accurate maps of sagebrush are needed to identify seasonal habitats of sage-grouse for the Bureau of Land Management
Purpose and benefits: extend presence modeling to map fractional cover of sagebrush in the Dakotas
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Use of artificial intelligence tools for optimization and documentation for computer codes [2024 INV#WO0000000109681]
Agency: Department of the Interior - USGS
Problem addressed: computer codes are needed that implement earthquake rupture forecasts and ground-motion models. This project uses ChatGPT to suggest optimizations and documentation for computer codes.
Purpose and benefits: time savings
Topic area: Science
Development stage: Pilot
First used: 2023-10-02
US federal AI use case: National-Extent Groundwater Quality Prediction for the National Water Census and Regional Integrated Water Availability Assessments [2024 INV#WO0000000109709]
Agency: Department of the Interior - USGS
Problem addressed: provide Nationally consistent predictions of groundwater quality (salinity and nutrients) relevant for human and ecological uses and its influence on surface-water
Purpose and benefits: Nationally consistent predictions of groundwater quality can be integrated into comprehensive water-availability assessments including the National Water Census and regional Integrated Water Availability Assessments
Topic area: Science
Development stage: Pilot
First used: 2021-10-01
US federal AI use case: Data-Driven Streamflow Drought [2024 INV#WO0000000109714]
Agency: Department of the Interior - USGS
Problem addressed: streamflow drought forecasts
Purpose and benefits: ability to provide drought forecasts using data-driven, machine learning approaches for USGS gage locations across the continental U.S.
Topic area: Science
Development stage: Pilot
First used: 2022-10-03
US federal AI use case: FEMA mixed population flood-frequency analysis [2024 INV#WO0000000109723]
Agency: Department of the Interior - USGS
Problem addressed: Classification of historical floods based on causal mechanisms to support improved estimation of flood reoccurrence intervals
Purpose and benefits: improved estimation of flood reoccurrence intervals
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Avian population estimates from passive acoustic monitoring [2024 INV#WO0000000109725]
Agency: Department of the Interior - USGS
Problem addressed: Reliable estimates of avian abundance from acoustic recordings
Purpose and benefits: improved estimates of avian abundance
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Prioritized Constituents: Sediment [2024 INV#WO0000000109726]
Agency: Department of the Interior - USGS
Problem addressed: Regional prediction of suspended sediment concentration in unmonitored rivers to characterize sediment transport in the Delaware, Illinois, and Colorado River Basins.
Purpose and benefits: ability to characterize sediment transport in the Delaware, Illinois, and Colorado River Basins
Topic area: Science
Development stage: Pilot
First used: 2023-10-02
US federal AI use case: PAWSC Ecotoxicology PFAS Machine Learning [2024 INV#WO0000000112908]
Agency: Department of the Interior - USGS
Problem addressed: assess the ecological health risk of PFAS in Pennsylvania stream surface water
Purpose and benefits: predict potential PFAS exposure effects in unmonitored stream reaches
Topic area: Science
Development stage: Deployed
First used: 2024-12-09
US federal AI use case: Parsing large quantities of text data [2024 INV#WO0000000113692]
Agency: Department of the Interior - USGS
Problem addressed: The goal is to assess how news sources discuss water events, how this relates to water availability and vulnerability, and how these patterns vary over time/space.
Purpose and benefits: time savings, without AI this would require reading through thousands of news articles to extract relevant information including mention of a specific the hazard (e.g., drought, flood, HABs), geographic region, organization, and other topical keywords.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Use modern AI/ML approaches to gain insight into USGS unstructured data such as text [2024 INV#WO0000000131706]
Agency: Department of the Interior - USGS
Problem addressed: There are a lot of unstructured data within USGS. Current efforts are to manually extract and analyze that data. We want to apply modern AI/ML approaches such as RAG (Retrieval-Augmented Generation) to gain insights from that unstructured data.
Purpose and benefits: gain insights from that unstructured data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: USGS Azure OpenAI ChatGPT [2024 INV#WO0000000154392]
Agency: Department of the Interior - USGS
Problem addressed: Various groups within USGS need a generic ChatGPT API to call from IDEs and various applications.
Purpose and benefits: Cost savings, USGS sought to implement its own pay-as-you-go model in the DOI Azure tenant.
Topic area: Science
Development stage: Pilot
First used: 2025-02-03
US federal AI use case: Google Vertex AI Document workbench [2024 INV#WO0000000154393]
Agency: Department of the Interior - USGS
Problem addressed: The USGS Energy Resource Program needs to use Google Vertex AI document workbench to perform data rescue on some old paper USGS publication tables with oil and gas production data from the 1940s-1980. This data is not available in digital form.
Purpose and benefits: data rescue
Topic area: Science
Development stage: Pilot
First used: 2025-02-03
US federal AI use case: Google Cloud Vision [2024 INV#WO0000000154445]
Agency: Department of the Interior - USGS
Problem addressed: USGS Water Mission Area needs text extraction from publicly available topographic maps as a callable function of an application.
Purpose and benefits: time savings, cost savings
Topic area: Science
Development stage: Pilot
First used: 2025-02-03
US federal AI use case: Harmful Algal Bloom prediction and detection system for Williams Fork Reservoir [2024 INV#WO0000000184339]
Agency: Department of the Interior - USGS
Problem addressed: We have used remote sensing products to detect harmful algal blooms throughout the Upper Colorado Basin. However, there have been multiple algal blooms in the Williams Fork Reservoir that have remained undetected.
Purpose and benefits: leverage field data collected with satellite overpasses to tease out what may be causing this discrepancy. We want to leverage AI/ML to see if we can build a new or improve existing models to boost the signal in this high-altitude reservoir
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning approach to predict the composition of seafloor massive sulfide deposits [2024 INV#WO0000000108420]
Agency: Department of the Interior - USGS
Problem addressed: predict the composition of seafloor massive sulfide deposits
Purpose and benefits: time and cost savings
Topic area: Science
Development stage: Pilot
First used: 2023-07-29
US federal AI use case: ChatGPT to write Python scripts for ArcGIS Pro Maps to be CVD-Friendly [2024 INV#WO0000000107402]
Agency: Department of the Interior - USGS
Problem addressed: automate the process of changing colors of planetary geologic map units within ArcGIS pro so that they are all color-vision deficiency friendly
Purpose and benefits: time savings
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Automated Walrus Haulout Monitoring [2024 INV#WO0000000110052]
Agency: Department of the Interior - USGS
Problem addressed: provide a framework for using pre-trained image classification convolutional neural network CNN models to make predictions on unlabled image datasets to provide data for further analysis of walrus (Odobenus rosmarus) coastal haulout occupation
Purpose and benefits: time savings, reduce manual image review
Topic area: Science
Development stage: Deployed
First used: 2024-02-29
US federal AI use case: VoiceAtlas no-code chatbot framework
Agency: Department of the Interior - USGS
Problem addressed: Many business areas in USGS could benefit from AI tools, however do not have technical expertise on staff.
Purpose and benefits: VoiceAtlas is an out of the box no code solution for non-technical employees.
Topic area: Science
Development stage: Pilot
First used: 2024-11-15
US federal AI use case: ONRR Video Hosting Platform [OVHP][2024 Inv#WO0000000110488]
Agency: Department of the Interior - ONRR
Development stage: Retired
US federal AI use case: Video Creation and Editing[2024 Inv#WO0000000110494]
Agency: Department of the Interior - ONRR
Development stage: Retired
US federal AI use case: Machine Learning Model Optimization[2024 INV#WO0000000110563]
Agency: Department of the Interior - ONRR
Development stage: Retired
US federal AI use case: Image Generation and Audio Video Editing [2024 Inv#WO0000000110551]
Agency: Department of the Interior - ONRR
Development stage: Retired
US federal AI use case: Intelligent Optical Character Recognition [2024 INV#WO0000000110733]
Agency: Department of the Interior - IBC
Development stage: Retired
US federal AI use case: Development of a computer vision model to monitor for early detection of habitat loss across the landscape. [2024 INV#DOI-65]
Agency: Department of the Interior - FWS
Problem addressed: The U.S. Fish and Wildlife Service is planning to partner with the Chesapeake Conservancy, an NGO, to develop a computer vision model to monitor for early detection of habitat loss across the landscape, a significant threat to biodiversity, including threatened and endangered species.  By using a computer vision model one can rapidly identify and flag areas where habitat loss may be occurring due to natural or human-caused disturbances.  Early detection can facilitate rapid responses, when appropriate, or allow practitioners to accurately calculate habitat loss over time.  More accurate estimates of habitat loss allow for better management decisions and potentially shorter recovery times for threatened and endangered species.
Purpose and benefits: AI-powered computer vision enables early detection of habitat loss, allowing faster, more accurate responses to threats. This supports better conservation decisions and helps protect and recover threatened and endangered species.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Autonomous Drone Inspections
Agency: Department of the Interior - BSEE
Problem addressed: The Bureau of Safety and Environmental Enforcement within the Department Of Interior, is requesting a trade study of the feasibility of autonomously inspecting off-shore facilities that are non-boardable due to various hazards. These inspections are currently performed by inspectors at a stand off distance from the non-boardable facility on board boats, helicopters, or from land. The distance between the personnel and the facility reduces the quality of inspections that are possible for non-boardable facilities.
Purpose and benefits: Increase the inspection capabilities and efficiency of inspections through the use of small autonomous uncrewed aerial systems (sUAS).
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Well Activity Report Classification
Agency: Department of the Interior - BSEE
Problem addressed: Researching the use of Masked Language Models and Convolutional deep neural networks to identify classification systems for significant well event using data from Well Activity Reports.
Purpose and benefits: Enable quicker detection of significant well events to help BSEE personnel mitigate risks and address issues more efficiently.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Level 1 Survey Report Corrosion Level Classification
Agency: Department of the Interior - BSEE
Problem addressed: Offshore operators conduct Level 1 surveys annually to report on platform structural integrity, as mandated by 30 CFR 250.901(a)(7), and submit these surveys to BSEE. Each survey includes a corrosion assessment of the platform with accompanying photos. Each area is assigned a coating grade and are key indicators of a platforms overall structural health. Currently, BSEE manually reviews each report to determine if a platform requires further audits, a process that is both time and labor intensive.
Purpose and benefits: Support a more efficient and accurate review of Level 1 Survey photo corrosion levels
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Sustained Casing Pressure Identification [2024 Inv# WO0000000108777]
Agency: Department of the Interior - BSEE
Development stage: Retired
US federal AI use case: Well Risk Assessment [2024 Inv# WO0000000108776]
Agency: Department of the Interior - BSEE
Development stage: Retired
US federal AI use case: ROV Smart Touch Subsea Pipeline Inspections
Agency: Department of the Interior - BSEE
Problem addressed: The lack of efficiency and or capability for BSEE and the Oil and Gas industry to inspect bolt failures for underwater pipelines.
Purpose and benefits: Potentially enhance subsea pipeline inspections by integrating advanced robotics and machine learning technologies.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Remote Sensing Coastal Change - Shoreline Change
Agency: Department of the Interior - USGS
Problem addressed: Rapid classification of satellite imagery to determine shoreline change
Purpose and benefits: Quicker, more accurate identification of risks to public safety and infrastructure due to erosion or other shoreline changes.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Bird Nest Detection [2024 Inv#WO0000000110506]
Agency: Department of the Interior - NPS
Problem addressed: Monitoring of colonial nesting birds with manual photo processing takes substantial time and effort. The goal is to identify bird nests with an object detection model.
Purpose and benefits: Researchers will be able to monitor bird colonies and their populations more efficiently and consistently.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Use of AI to Enhance Flash Flood Forecast Tool [2024 Inv#WO0000000110323]
Agency: Department of the Interior - NPS
Problem addressed: There is an opportunity to better predict rainfall on a watershed scale in Great Smoky Mountains National Park and provide forecasts of flooding events with a goal of a 24+ hour lead time.
Purpose and benefits: Once we can implement and use the flood forecasting app, we anticipate being able to use it proactively to close at-risk sections of the park during forecast flooding events, saving lives and reducing risks to first responders.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: Public Comment Analysis Tool (PCAT): Leveraging Artificial Intelligence (AI) for Enhanced Efficiency  [2024 INV#WO0000000106351]
Agency: Department of the Interior - OS
Development stage: Retired
US federal AI use case: I-NEPA System: Leveraging Artificial Intelligence (AI) for Enhanced Efficiency [2024 INV# WO0000000111250]
Agency: Department of the Interior - OS
Development stage: Retired
US federal AI use case: Liable Party Research [2024 INV#WO0000000110496]
Agency: Department of the Interior - ONRR
Development stage: Retired
US federal AI use case: Data Extraction Using MS Power Automate AI Functionality [2024 INV#WO0000000110500]
Agency: Department of the Interior - ONRR
Development stage: Retired
US federal AI use case: Adobe Suite Applications
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Adobe Suite products, is to support the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) in enhancing the quality and efficiency of multimedia content handling within their operations. The out-of-the-box artificial intelligence features included in these Adobe applications assist in tasks like image enhancement, optical character recognition (OCR), content-aware editing, and document formatting. These AI capabilities enable ATF to prepare high-quality documents and multimedia files for various purposes By automating and improving these tasks, the AI tools help reduce manual workload, minimize errors, and increase operational efficiency. Overall, the use of AI in Adobe Suite products is designed to improve the effectiveness, accuracy, and professionalism of ATF's documentation and multimedia processing.
Purpose and benefits: The expected benefits of utilizing the AI features in Adobe Suite Products are broad and extend beyond FOIA-related activities. The AI capabilities in applications like Photoshop and Acrobat enhance productivity by automating routine tasks such as image editing, optical character recognition (OCR), and content-aware fill. This automation allows staff to process documents and multimedia content more quickly and accurately, leading to time savings and reduced operational costs. The improved efficiency helps in various functions, from preparing official documents to creating high-quality visual materials for communication purposes. Additionally, the AI tools contribute to better quality outputs, which can enhance public engagement and trust. Overall, the AI features in Adobe Suite Products support increased productivity, cost savings, and higher-quality work across a range of organizational activities.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Airlines Travel Intelligence Program
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in the Airlines Reporting Corporation Travel Intelligence Program is to examine travel data and highlight atypical routes or passenger movements more swiftly. Leveraging AI for pattern recognition and predictive insights reduces manual data inspection, speeds the identification of significant travel anomalies, and enables more prompt, well-informed decision-making. Overall, it refines resource deployment and fosters more accurate, efficient management of travel-related intelligence.
Purpose and benefits: The expected benefit is reduced effort identifying unusual travel scenarios, focusing on significant itineraries/individuals.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2023-01-01
US federal AI use case: Unmanned Aerial Systems (UAS)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: Airship is an enterprise video management platform used to manage, secure, store, and analyze video surveillance obtained through criminal investigations. The airship platform includes AI-based capabilities for optical character recognition (OCR) for car license plates, airplane tail numbers, etc. and object detection with customizable near-real time alerts.
Purpose and benefits: AI features help to support agent monitoring of surveillance video streams to ensure rapid notifications when predefined events occur that are pertinent to a criminal investigation.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Alation Data Catalog
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The AI within Alation’s Data Catalog is designed to make data easy to find, understand, and use. It leverages machine learning and natural language processing (NLP) to automatically organize and tag data, creating a “map” of available information from various sources within an organization. The goal is that anyone looking for specific data can use simple, plain-language search terms, and the AI will help them locate the most relevant information quickly.
Purpose and benefits: Enable users to quickly find relevant data across large, complex datasets, making information more accessible for decision-making. Additionally, Alation’s AI automates data organization and governance, helping to keep data accurate, up-to-date, and secure.  It also supports compliance and builds trust in data, empowering teams to make reliable, data-driven decisions.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2023-11-01
US federal AI use case: Axon FUSUS (via HIDTA partners)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: This capability is not provided or managed by ATF. It provides ATF staff access to Axon FUSUS systems which are provided and managed by High Intensity Drug Trafficking Area (HIDTA) partner agencies. FUSUS integrates video and other data from public safety systems to support law enforcement investigations and increase situational awareness for real-time operations. This includes AI-enabled  analysis of participating video streams, with configurable real-time notifications to law enforcement.
Purpose and benefits: Increased situational awareness for real-time law enforcement operations.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Azure Data Factory
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: ATF recently enabled Azure Data Factory subscription in Azure, which included smart and intelligent features like anomaly detection, data quality analysis, data completeness, and prediction. While we do not use these features directly, they are embedded within the tools.
Purpose and benefits: The embedded data analytic and data quality capabilities will increase the efficiency and effectiveness with which ATF is able to locate and analyze our data, and the quality and reliability of the resulting data.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Azure Zen 2 Storage
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: ATF recently enabled Storage subscription in Azure, which included smart features automatic data indexing. While we do not use these features directly, they are embedded within the tools, enabling automatic data indexing based on the data fed into the system.
Purpose and benefits: Automatic data indexing increases the efficiency and effectiveness with which ATF is able to locate and analyze our data.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Bloomberg Government
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Bloomberg Government (BGOV) is to enhance policy and regulatory analysis by automating data aggregation, pattern detection, and trend forecasting. Using AI, the system refines vast datasets into actionable insights, helping users quickly understand emerging issues and legislative shifts.
Purpose and benefits: More informed strategic planning, better resource allocation, and improved overall comprehension of complex policy environments.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2023-06-01
US federal AI use case: CargoNet
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in CargoNet is to detect and interpret patterns within logistics and theft incident data automating the process of identifying unusual activities or recurring risks.
Purpose and benefits: The expected benefit is focusing efforts on deviating areas/goods/patterns to improve effectiveness of subsequent steps.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Cell Hawk (via HIDTA partners)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: This capability is not provided or managed by ATF. It provides ATF staff read-only access to Cell Hawk data which is provided and managed by High Intensity Drug Trafficking Area (HIDTA) partner agencies.
Purpose and benefits: Link and trend analysis results provided by this system provide law enforcement personnel with increased insights into subjects' activities in the context of criminal investigations.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Coinbase
Agency: Department of Justice - Department of Justice / ATF
Development stage: Retired
Risk category: High-impact
US federal AI use case: Digital.ai
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: Digital.ai is used as ATF's enterprise tool for managing agile software development projects. It is used to capture user stories (requirements) and manage the processes of prioritization, implementation, testing, and close-out of the user stories. None of the AI features available within digital.ai are currently in use, but the use case is being reported because they are available within the product. AI features involve analysis of project status, supporting automated test generation, and automating software releases. ATF uses other non-AI products for these purposes.
Purpose and benefits: ATF is not using any of the available AI features.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Dun & Bradstreet Business Establishments Data
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Dun & Bradstreet Business Establishments Data is to analyze business information, detect irregularities, and assess potential risks more efficiently. AI-driven data integration, anomaly detection, and trend analysis automate what would otherwise be intensive manual evaluations.
Purpose and benefits: The expected benefit is more targeted attention on atypical organizations, improving efficiency and reducing randomness.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Axon Evidence.com
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: ATF's use of Axon body-worn cameras and the associated evidence.com service are integral to ensuring ATF's compliance with DOJ policy documented in DOJ OIG, Inspector General Manual, Volume III, Chapter 236, Body Worn Camera Program. Body cam videos are transferred to the Axon Evidence.com service, which includes AI-based features for performing recognition of heads in videos for the purpose of redaction. ATF has performed limited testing of these capabilities, but is not operationally using them.
Purpose and benefits: Evidence.com provides AI-based features that identify the presence of heads in videos and can perform automated redaction. This would increase the efficiency of redacting video evidence for release. However, ATF is not operationally using them.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2020-01-01
US federal AI use case: Federal Docket Management System
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: This system allows agencies to receive comments that are submitted electronically in response to rulemaking initiatives. The system has features that categorize comments based on text analytics, allows agencies to make those comments public facing or redact them, and allows agencies to download the comments.
Purpose and benefits: This product permits agencies to review the electronic comments received in response to rulemaking. The features in FDMS allow agencies to bulk post up to 1,000 comments allowing the public to see their comments faster.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: FINDER
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in FINDER is to sift through violent crime and firearms trafficking data more efficiently. By employing machine learning to highlight significant patterns, suspects, or trends, it automates tasks that would normally require manual, resource-intensive reviews. This leads to quicker identification of essential insights, improves operational effectiveness, and ensures that effort is directed at the most pertinent leads, enhancing both speed and precision in investigative processes.
Purpose and benefits: The expected benefit is more strategic attention to areas that may reduce negative outcomes or enhance preparedness.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: First Two
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in First Two is to link individuals to specific locations and visualize spatial relationships that might be important. By automating map-based data correlation, pattern discovery, and geospatial analysis, the system reduces manual data plotting and interpretation. This results in quicker recognition of significant activity areas, improved allocation of resources to critical locations, and overall enhancement of operational responsiveness and strategic planning.
Purpose and benefits: The expected benefit is more efficient focus on frequently visited places/people, enhancing resource allocation where location matters.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2021-01-01
US federal AI use case: LexisNexis Accurint
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in LexisNexis Accurint is to merge and analyze an array of public and proprietary records to build a clearer picture of individuals and entities. By automating data integration, filtering, and pattern recognition, it expedites the identification of relevant subjects and connections. This advanced approach reduces manual searching, enhances accuracy in linking data points, and strategically directs focus toward high-value leads, improving overall investigative efficiency and decision-making.
Purpose and benefits: The expected benefit is accelerated ID of important figures/relationships, skipping disjointed dataset searches.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: LexisNexis Babel Street
Agency: Department of Justice - Department of Justice / ATF
Development stage: Retired
Risk category: High-impact
US federal AI use case: Mark43 Public Safety Records
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Mark43 Public Safety Records is to organize, categorize, and correlate extensive law enforcement data from various sources. By employing AI-driven classification, entity resolution, and data linking, it reduces the manual workload needed to extract meaningful insights. This empowers users to identify key relationships and patterns more rapidly, increases data accuracy, and supports more strategic use of investigative resources, ultimately improving both timeliness and quality of public safety operations.
Purpose and benefits: The expected benefit is better informed decisionmaking, allowing focus on data likely to yield meaningful insights.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: National Insurance Crime Bureau ISO ClaimSearch
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in National Insurance Crime Bureau ISO ClaimSearch is to analyze insurance claim data and highlight suspicious patterns or anomalies more efficiently. By applying AI-driven pattern recognition, risk assessment, and anomaly detection, it streamlines what would be a tedious manual review process. This leads to faster fraud identification, more accurate targeting of problematic claims, and better resource utilization, ultimately strengthening investigative outcomes.
Purpose and benefits: The expected benefit is more effective resource use, targeting claims that differ from typical patterns rather than all equally.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2020-01-01
US federal AI use case: Operational Planning Analytics Risk Management Solution (OPARMS)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The Operational Planning Analytics Risk Management Solution (OPARMS) creates op plans within ATF's case management system, eliminating the previous system for generating op plans. ATF anticipates that OPARMS will also provide dashboard overviews of the op plans and risk data for all op plans.
Purpose and benefits: Improve operational planning, data collection, and risk mitigation. Reduce time to fill out op plans. Increase accuracy of data entered into ops plans by populating info from other ATF systems. Reduce approval times by routing and tracking op plans through OPARMS. Increase collection of operations planning and after action reports. Collect and store data to begin developing the analytics for calculating and identifying risk in operations allowing team members to better mitigate those risks.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Palantir (via access to external state LE system)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: This use case is being reported because Palantir has AI-based capabilities. However, ATF only accesses Palantir through task force partnerships with the a state law enforcement (LE) partner. The state LE agency uses Palantir as their case management system, and ATF's use is limited to searching for information which the state LE agency chooses to share with law enforcement partners. ATF has no involvement with or any knowledge of use of AI features by the state agency which runs the system.
Purpose and benefits: ATF use of the state LE partner Palantir system is limited to searching for information which the state agency chooses to share with law enforcement partners. ATF has no involvement with, any knowledge of, or any expected benefits from use of AI by the state agency which runs the system.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: ShotSpotter (via access to external state/local systems)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: Rapidly locate gunshots and activity of potential investigative interest to alert relevant law enforcement agencies.
Purpose and benefits: Rapid detection of gunshots, which can help to decrease the time to respond to a violent crime.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Southwest Border Transaction Record Analysis Center (SWBTRAC)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Southwest Border Transaction Record Analysis Center (SWBTRAC) is to monitor cross-border financial transactions and identify unusual activities more efficiently. By integrating AI-driven anomaly detection, trend analysis, and risk scoring, it simplifies manual review and directs attention to truly irregular transfers. This approach ensures that investigative focus is applied judiciously, improves accuracy, and enhances strategic use of resources in addressing cross-border financial concerns.
Purpose and benefits: The expected benefit is faster recognition of outlier scenarios, focusing on meaningful transactions rather than all equally.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Spokeo
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Spokeo is to aggregate and enrich publicly accessible personal data to create comprehensive profiles. AI-based entity resolution, data linkage, and pattern recognition automate the task of piecing together scattered details. This significantly reduces manual workload, sharpens the accuracy of identifying individuals of interest, and ensures that investigative energies are invested where they can yield the most valuable insights.
Purpose and benefits: The expected benefit is quicker access to comprehensive background details, easily identifying individuals of interest.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Thomson Reuters CLEAR
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Thomson Reuters Clear is to consolidate and analyze diverse records, creating coherent profiles of individuals and entities. By applying AI-driven data integration, entity resolution, and intelligent filtering, it automates traditionally manual tasks. This leads to faster identification of subjects of interest, improved accuracy in linking related data points, and a more strategic use of time and resources. Ultimately, AI integration enhances investigative effectiveness, reduces errors, and supports more targeted research.
Purpose and benefits: The expected benefit is reduced manual effort, enabling quicker discovery of relevant profiles/relationships.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: TransUnion TLOxp Online Investigative Services
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in TransUnion TLOxp Online Investigative Services is to gather and analyze diverse personal, financial, and asset-related data more effectively. By implementing AI for entity resolution, risk scoring, and pattern detection, it replaces manual data handling with automated insights. This accelerates the discovery of meaningful leads, reduces errors, and ensures attention is concentrated on cases truly warranting further review, improving both accuracy and resource allocation.
Purpose and benefits: The expected benefit is better time/effort use, focusing on subjects/data points that seem more meaningful.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01