GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: OCC.DocChat
Agency: Department of the Treasury - Office of the Comptroller of the Currency (OCC)
Problem addressed: GenAI solution for uploading documents to Personal and Group workspaces and facilitating chat interactions.
Purpose and benefits: The AI is designed to solve the problem of managing and interacting with documents by enabling users to upload documents to Personal and Group workspaces and facilitating chat interactions based on the content of these documents. This streamlines document management and enhances collaborative communication.
Topic area: IT
Development stage: Pilot
Impact assessment: This GenAI tool serves as the foundation of other use cases at the OCC.
First used: 2025-08-18
US federal AI use case: Resource Assignment Optimization
Agency: Department of the Treasury - Office of the Comptroller of the Currency (OCC)
Problem addressed: The AI is intended to reduce the manual effort involved in reviewing open positions and identifying available staff who may be a good fit. Currently, analysts and managers must sort through resource lists, skill profiles, and job requirements individually. The AI helps automate this matching process by applying predefined rules and logic, making it easier to identify potential matches quickly and consistently.
Purpose and benefits: The AI improves workforce efficiency by helping the agency deploy staff more quickly and effectively, which supports timely supervision activities and strengthens overall operational readiness. By reducing administrative workload, analysts and managers can spend more time on mission-critical responsibilities. Ultimately, better resource alignment contributes to more effective oversight of the banking system, which benefits the public through a stable and well-regulated financial environment.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: This use case matches open resources with available job opportunities and does not make hiring or employment decisions that could impact rights or access to opportunities.
US federal AI use case: Hand-written text extraction using Textract for the Public Engagement with Science Initiative
Agency: National Science Foundation - Office of Integrative Activities (OIA)
Problem addressed: OIA Team    proposes using a Commercial Off the Shelf Amazon Web Services (AWS) product, Textract, as a character recognition tool to convert handwritten text into machine-encoded text for a postcard. Handwritten text will be an open format question for science and engineering, and some optional check boxes for demographic and other data questions.
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: Helpdesk chat assistant for common data system tasks
Agency: Environmental Protection Agency - OFA
Problem addressed: The Chat Bot implementation increases Help Desk tier 0 resolution, resulting in higher customer satisfaction and better use of Help Desk resources.
Purpose and benefits: Improving customer wait time.  Increasing Helpdesk agents' availability to resolve more complex IT incidents.
Topic area: IT
Development stage: Deployed
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
First used: 2025-09-12
US federal AI use case: FFEO TSCA investigation into privatized military housing property management companies
Agency: Environmental Protection Agency - OECA
Problem addressed: 1)	Reviewing spreadsheets or documents for TSCA Renovation, Repair, and Painting work descriptions 
2)	Reviewing documents containing leases for Lead Disclosure Rule violations
3)	Reviewing pictures and video for TSCA Renovation, Repair, and Painting work descriptions
4)	Drafting enforcement actions with the understanding that the final determination of compliance and enforcement actions is inherently a government function.
Purpose and benefits: Makes FFEOs law enforcement processes and functions more efficient.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Briefcam
Agency: Environmental Protection Agency - OECA
Problem addressed: Speed the review and improve the accuracy of captured surveillance footage.
Purpose and benefits: This product will help increase case investigation speed by greatly reducing the amount of time Special Agents spend reviewing video footage.
Topic area: Law Enforcement
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
US federal AI use case: NSF AI-Ready Research Performance Progress Reports (RPPR) Data
Agency: National Science Foundation - Office of Chief Information Officer (OCIO)
Problem addressed: OCIO is working to provide AI-ready RPPR data – a key component for determining outcomes of NSF investments.
Note: Information in the RPPR may be used to identify outputs  and be linked to other data sets (e.g., NSF PAR, Patent, etc.) to assess NSF's "broader impacts"; however,  it is not currently available in an accessible format.
Problem Statement:
RPPR data, a critical resource for tracking the outcomes of NSF-funded projects.
Data are currently stored in various formats across multiple locations, making it inaccessible for analytic purposes and difficult to reach without impact to current operations.
The lack of centralized, accessible data limits NSF's ability to assess impacts, make informed decisions, and fully leverage data for insights.
Request approval to use AWS Bedrock, AWS SageMaker, AWS Neptune & AWS Comprehend from AIDG (AI Data Governance).
Development stage: Pre-deployment
First used: 2025-06-01
US federal AI use case: Technology, Innovation and Partnerships (TIP) Microsoft (MS) Copilot Pilot
Agency: National Science Foundation - Directorate for Technology, Innovation, and Partnership (TIP)
Problem addressed: Copilot can be used to rewrite and generate text based on user prompts in Microsoft 365 services, including Microsoft Word, Microsoft Excel, and PowerPoint.  Copilot for Microsoft 365 uses Microsoft Graph, an API, to evaluate context and available Microsoft 365 user data before modifying and sending user prompts to the language model.  After receiving its output, Microsoft Graph performs additional context-specific processing before sending the response to Microsoft 365 apps to generate content.
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: OCC Guidance Summarization
Agency: Department of the Treasury - Office of the Comptroller of the Currency (OCC)
Problem addressed: OCC staff must frequently review a wide range of OCC regulatory sources—Handbooks, Bulletins, statutes, old Circulars, Interpretive Letters, Corporate Actions, and related guidance—to understand requirements and develop supervisory positions. Manually locating, reading, and synthesizing these materials is time-consuming and can slow the early stages of analysis. The AI is intended to streamline this initial research by quickly identifying relevant sources and summarizing key regulatory points.
Purpose and benefits: The AI helps staff rapidly gain orientation to applicable regulatory frameworks, reducing the time spent gathering and processing foundational information. This supports the agency’s mission by promoting more timely and consistent application of asset management regulations and supervisory expectations. The public benefits through enhanced oversight of fiduciary and asset management activities, contributing to stronger investor protection and safer banking practices.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: This use case does not make or influence decisions that affect rights, access to services, safety, or any other outcomes defined in the high-impact criteria, and is limited to routine operational support.
US federal AI use case: A document summarizer via Natural Language Processing
Agency: National Science Foundation - Directorate of Engineering (ENG)
Problem addressed: The AI system utilizes state-of-the-art transformer-based models (e.g., BART, Pegasus, T5, SciBERT) to perform text summarization. These models are deployed entirely within the agency’'s secure firewalls, ensuring no external data exposure and no external access. BART, Pegasus, T5 and SciBERT are state-of-the-art, open-source AI models designed for Natural Language Processing (NLP) tasks. The model will be able to summarize lengthy internal documents, helping employees quickly extract key insights from a large volume of information.  This tool automates summarization, reducing the time employees spend reading lengthy documents, enhancing productivity by accurately identifying key points in such documents. The traditional extractive summarization (e.g., via Term Frequency-Inverse Document Frequency TF-IDF) selects key sentences verbatim from the source, often misses several key elements of the document, and results in an inaccurate representation of the content. Unlike traditional extractive summarization, generative summarization (used by BART, Pegasus and T5) creates modified sentences that better capture the meaning and context of the document. This leads to more natural, coherent and concise summaries which enhance readability and contextual accuracy.
Development stage: Pre-deployment
First used: 2025-06-01
US federal AI use case: Document similarity analysis via Natural Language Processing
Agency: National Science Foundation - Directorate of Engineering (ENG)
Problem addressed: The AI system utilizes a sentence-transformer model (all-mpnet-base-v2) and a statistical keyword method (TF-IDF) to perform document similarity tasks. These models are deployed entirely within the agency’s secure firewalls, ensuring no external data exposure and no external access. These AI models are designed for natural language processing (NLP) tasks. Via all-mpnet-base-v2, the model will use utilize deep learning models to capture semantic meaning, ideal for conceptual similarity beyond exact wording. Via Term Frequency-Inverse Document Frequency (TF-IDF), the model will focus on lexical overlap and is effective for direct technical document comparison. Both models calculate Cosine similarity, with scores ranging from -1 to 1. Scores can be interpreted as follows: identical (1), highly similar (0.8-0.99), somewhat similar (~0.5), mostly dissimilar (0.1-0.3), completely dissimilar (0) and opposite (-1 to 0). Document similarity analysis can significantly boost productivity since a large volume of documents can quickly and efficiently be analyzed for extent of contextual or lexical similarity and can aid in routine planning for proposal administration. Cosine similarity is also a well-established method to quantify similarity and is already used within NSF tools.
Development stage: Pre-deployment
First used: 2025-06-01
US federal AI use case: Microsoft 365 (M365) AI Builder pre-built Generative Pre-trained Transformer (GPT) for Summarization
Agency: National Science Foundation - Office of Integrative Activities (OIA)
Problem addressed: OIA team requests  to use the existing AI Builder pre-built Generative Pre-Trained Transformer (GPT)  functions within Microsoft Power Platform to perform summarization tasks on text and tag data in Microsoft 365 (M365) to support:
1. Creation of Planner tasks from emails and
2. Creation of draft weekly and monthly accomplishment reports from text and tag data in email, meeting invitations, and tasks.

The scope of data is limited to that available to an individual M365 account.
Development stage: Deployed
First used: 2025-06-01
US federal AI use case: RelativityOne Gov aiR
Agency: Environmental Protection Agency - OFA
Problem addressed: aiR for Review is designed to perform an initial first-pass review in legal case or other document review, such as a FOIA matter. Relativity's "aiR for Review" identifies potentially relevant documents, describes why they are relevant using natural language, and demonstrates this relevance using citations from the document.
Purpose and benefits: aiR for Review can improve efficiency in document reviews, potentially enabling the Agency to more quickly and efficiently respond to FOIA and other eDiscovery requests.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Comparison of proposal similarities at different levels of National Science Foundation (NSF) organization
Agency: National Science Foundation - Directorate for Biological Sciences (BIO)
Problem addressed: The goal of the analysis is to compare how any two programs, divisions or , directorates in NSF are similar to each other to assess uniqueness and overlap between units. The comparison is based on a similarity score (cosine similarity) of the embeddings of any two proposal summaries. The embeddings are generated using sentence transformer models.
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: Summarize and draft response to comments
Agency: Environmental Protection Agency - Region 8
Problem addressed: summarize large volume of comments reproducibly
Purpose and benefits: we can get many thousand comments. this is meant to help us summarize them to respond too.
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
First used: 2025-09-01
US federal AI use case: Document comparison
Agency: Environmental Protection Agency - Region 8
Problem addressed: Review and comparison of two documents
Purpose and benefits: cutting back on the number of templates that we maintain.
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
First used: 2025-10-01
US federal AI use case: Employee Task Organization
Agency: Department of the Treasury - Office of the Comptroller of the Currency (OCC)
Problem addressed: Assigning employees to tasks requires balancing multiple factors such as availability, workload, skill sets, and operational constraints. This process is often manual, time-consuming, and prone to inefficiencies, especially when coordinating across teams or during periods of high activity. The AI is intended to streamline workforce allocation by evaluating these constraints and recommending optimal task assignments, reducing administrative burden on managers.
Purpose and benefits: The AI improves operational efficiency by helping managers make faster, more consistent assignment decisions that align resources with priorities. This supports the agency’s mission by ensuring that work is distributed effectively, reducing bottlenecks, and enabling staff to focus on higher-value activities. Ultimately, the public benefits from more responsive and timely execution of supervisory and regulatory responsibilities.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: This use case helps organize work and does not produce decisions that affect rights, benefits, access to services, safety, or any other outcomes defined in the high-impact criteria.
US federal AI use case: OPP Document Data Extraction
Agency: Environmental Protection Agency - OCSPP
Problem addressed: Manual staff data entry of values and phrases currently only available by opening and reading each document
Purpose and benefits: Cost savings in contract spending for mania data entry and resource/time savings for EPA staff who will not need to read documents and enter data.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Energy Star Awards Application Profile Drafting
Agency: Environmental Protection Agency - OAR
Development stage: Retired
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Natural Gas Star Videos
Agency: Environmental Protection Agency - OAR
Development stage: Retired
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Transitioning historical help desk knowledge to new help desk software
Agency: Environmental Protection Agency - OAR
Development stage: Retired
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Emission monitoring program knowledge management system search and summary
Agency: Environmental Protection Agency - OAR
Development stage: Retired
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Identifying outliers in reported emissions data
Agency: Environmental Protection Agency - OAR
Development stage: Retired
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: AI Rules
Agency: Environmental Protection Agency - OFA
Problem addressed: To speed up the time it takes to manually read and process public comments on proposed rules.
Purpose and benefits: Faster response to public comments on proposed rules. Cost savings from not having to outsource the work to contractors.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Predictive Analytics Supporting GHGRP Data Review
Agency: Environmental Protection Agency - OAR
Development stage: Retired
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: ServiceNow GenAI "Now Assist"
Agency: National Science Foundation - Office of Information and Resource Management (OIRM)
Problem addressed: ServiceNow GenAI is a FedRAMP High certified system to accelerate digital transformation using Now Assist. Automatically generate content such as responses, work notes, and knowledge base articles related to customer service experience. Increase productivity and speed of delivery  with intelligent recommendations. Resolve issues swiftly with chatbots that understand human language.
Development stage: Deployed
First used: 2025-06-01
US federal AI use case: Regulation Summarization
Agency: Department of the Treasury - Office of the Comptroller of the Currency (OCC)
Problem addressed: Regulatory updates and final rules associated with the regulatory programs are extensive, complex, and often highly technical. Staff must manually review lengthy documents to extract relevant requirements, interpret changes, and understand impacts on supervisory processes. This manual approach is time-consuming and can delay implementation planning. The AI is intended to streamline this process by efficiently summarizing regulatory text and highlighting key provisions that matter most to examiners and policy staff.
Purpose and benefits: By accelerating comprehension of regulatory materials, the AI helps staff more quickly understand updates and incorporate them into supervisory activities. This supports the agency’s mission by enhancing consistency, improving timeliness of regulatory interpretation, and reducing administrative burden. For the public, clearer and more efficient implementation and can contribute to more consistent enforcement and better support for community investment outcomes.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: This use case aids in the interpretation or summarization of regulatory information and does not generate decisions that affect rights, access to services, safety, or any other outcomes defined in the high-impact criteria.
US federal AI use case: Semantic search of qualitative data
Agency: Environmental Protection Agency - OFA
Problem addressed: Replace manual search of a database/Excel file with qualitative data with a "semantic search" (think of it like a Google search for your Excel file's qualitative data).
Purpose and benefits: Reduced response times to leadership questions on database content; increased time spent on human analysis instead of searching for data; identifying overlapping/duplicative data; more accurate queries of qualitative data.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: Agency Records Management System
Agency: Environmental Protection Agency - OFA
Problem addressed: reduce burden on staff when identifying appropriate records schedules
Purpose and benefits: cost savings, reducing administrative burden on staff and improving accuracy of records management
Topic area: Service Delivery
Development stage: Deployed
First used: 2023-12-11
US federal AI use case: CUI Marking Tool
Agency: Environmental Protection Agency - OFA
Problem addressed: Detection, identification and protection of sensitive and confidential information
Purpose and benefits: Improved cybersecurity through sensitive information distribution awareness
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
US federal AI use case: AI-Enhanced Facility Finder
Agency: Environmental Protection Agency - OECA
Problem addressed: Many facilities are required to register for industrial stormwater permits but do not do so. To find unpermitted facilities, EPA personnel use approaches that are time-consuming, inefficient, and infeasible to scale to the millions of facilities in the U.S.
Purpose and benefits: AI-Enhanced Facility Finder saves time for EPA employees by identifying facilities most likely to lack necessary industrial stormwater permits, which are then reviewed by humans. Testing at the state level has shown these identifications increase the number of facilities that are permitted and monitored, resulting in cleaner water for Americans.
Topic area: Law Enforcement
Development stage: Pilot
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
First used: 2025-07-01
US federal AI use case: Living Literature Review: Semi-automated Literature Screening
Agency: Environmental Protection Agency - OAR
Problem addressed: The Agency conducts reviews of scientific literature in many contexts, including periodic revision of NAAQS as mandated by the Clean Air Act, other standards, but also one-time assessments of published evidence. Given the scope of some of those reviews and the very large of peer-reviewed articles to consider, a system was required to rank and prioritize them for faster screening, based on past reviews and ongoing expert criteria.
Purpose and benefits: Faster, more responsive evidence reviews.
Topic area: Science
Development stage: Deployed
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
First used: 2023-07-05
US federal AI use case: Using machine learning methods to analyze drivers of water quality
Agency: Environmental Protection Agency - AO
Problem addressed: To increase our understanding of drivers of water quality.
Purpose and benefits: Use of these methods will help better inform water quality management activities.
Topic area: Energy and Environment
Development stage: Deployed
Impact assessment: The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
First used: 2024-01-01
US federal AI use case: RCRA PA
Agency: Environmental Protection Agency - OECA
Problem addressed: Risk scoring of Large Quantity Generators (LQGs) to support RCRA inspections
Purpose and benefits: Reducing staff time and better identification of potential violators
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2022-10-01
US federal AI use case: CWA PA
Agency: Environmental Protection Agency - OECA
Problem addressed: Reducing staff time and better identification of potential violators
Purpose and benefits: Identification of high risk NPDES facilities that don't submit DMR's
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: Budget Area Topic Classification Tool
Agency: National Science Foundation - Directorate for Computer and Information Science and Engineering (CISE)
Problem addressed: The Budget Area Topic Classification Tool uses a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model ('bert-base-uncased') to classify proposals into 1 of 8 budget areas: Advanced Manufacturing, Artificial Intelligence, Advanced Wireless, Clean Energy, Microelectronics & Semiconductors, Quantum, or Trustworthy AI. This approach is semi-supervised in nature as the training data is comprised of pre-tagged Computer and Information Science and Engineering (CISE)  proposals from prior years. Upon training the model to learn the multi-label structure of the underlying data, the model's accuracy and discriminatory power was tested using a hold-out sample (i.e., 80/20 split). The model outputs a numerical vector of probabilities representing the likelihood that a given proposal belongs to 1 or more categories. A cut-off value can then be set for determining the final classification for a given proposal.
Development stage: Pre-deployment
First used: 2023-06-01
US federal AI use case: CAA PA
Agency: Environmental Protection Agency - OECA
Problem addressed: Reducing staff time and better identification of potential violators
Purpose and benefits: Risk scoring of Majors and Synthetic Minor Facilities to support CAA inspections
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: RMP Risk Modeling
Agency: Environmental Protection Agency - OECA
Problem addressed: To identify facilities with Risk Management Plans (RMP) under the Clean Air Act (CAA) that have an elevated risk of accidents
Purpose and benefits: Reducing staff time and better identification of potential violators
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: AI voice over
Agency: National Science Foundation - Office for Legislative and Public Affairs (OLPA)
Problem addressed: The AI system generates temporary synthetic voice-over using text-to-speech services (e.g., Eleven Labs, WellSaid Labs) to convert scripts into placeholder audio for video drafts. These AI-generated voice-overs are used solely for timing and content review during production and are replaced with professional human voice-over before public release.
Development stage: Pilot
First used: 2023-06-01
US federal AI use case: Regulation Statutory and Common Name Indexing
Agency: Department of the Treasury - Office of the Comptroller of the Currency (OCC)
Problem addressed: Compliance OCC staff frequently need to reference regulations by both their alphabetical identifiers (e.g., Regulation Z) and their full statutory names. Manually searching for these connections across regulatory materials is repetitive and time-consuming. The AI is intended to simplify this process by quickly generating a clear mapping between alphabetical regulation names and their corresponding statutory titles, improving speed and accuracy during compliance reviews.
Purpose and benefits: The AI enhances staff efficiency by enabling faster access to accurate regulatory identifiers, reducing time spent on basic regulatory lookups. This supports the agency’s mission by improving consistency and reducing errors in examinations, training materials, and supervisory communications. The public benefits from more precise and efficient regulatory oversight, contributing to stronger consumer protections.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: This use case does not make decisions or affect anyone’s rights, benefits, access to services, or other areas defined as high-impact—it simply provides information without influencing outcomes.
US federal AI use case: AskBEP AI ChatBot
Agency: Department of the Treasury - Bureau of Engraving and Printing (BEP)
Problem addressed: The AskBEP chatbot addresses the challenge of quickly and consistently accessing policy information within the BEP community. Employees often need clear, accurate answers without having to search through lengthy documents or rely on staff availability. The AI solves this by providing an always-available, conversational tool that retrieves and explains relevant policy guidance in plain language.
Purpose and benefits: The AskBEP chatbot improves accessibility, consistency, and efficiency by providing 24/7 conversational access to accurate policy information. The AskBEP chatbot also automates common inquiries while ensuring consistent, accessible policy information.
Topic area: IT
Development stage: Pilot
Impact assessment: The AskBEP chatbot tool is designed to provide policy related information to the BEP community in a convenient and conversational format. Its function is strictly informational and does not involve processing or storing PII or sensitive data. The chatbot does not make determinations that affect individuals' civil rights, personal safety. financial status, or access to essential government sources.
First used: 2025-08-25
US federal AI use case: Sub-Asset Maintenance Model
Agency: Department of the Treasury - Bureau of Engraving and Printing (BEP)
Problem addressed: This model helps reduce downtime by forecasting when sub-asset machines will need maintenance, enabling proactive servicing and improved asset reliability.
Purpose and benefits: SAMM model helps forecast when sub-asset machines will require maintenance, reducing unexpected breakdowns, improving asset reliability, and optimizing maintenance schedules to support mission continuity.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Impact assessment: This use case is not high-impact because it focuses on a narrow operational task with limited enterprise-wide decision value or strategic impact. It proactively predict the mileage until the next maintenance will be needed on a sub asset machine.
First used: 2024-04-24
US federal AI use case: Ink and Paper Consumption Forecast (IPCF)
Agency: Department of the Treasury - Bureau of Engraving and Printing (BEP)
Problem addressed: The Ink and Paper Consumption Forecast (IPCF) AI model is intended to predict: 1. Ink mileage predictions based on ink type and associated denom; 2. Ink consumption predictions based on number of sheets; 3. Paper consumption predictions given a time period; 4. Ink consumption predictions given a time period
Purpose and benefits: The IPCF Predictive Analytics Model contributes to cost efficiency and supply chain resilience by predicting ink mileage based on ink type and denomination, as well as forecasting ink and paper consumption over time and by sheet volume. The model helps ensure accurate procurement, reduce material waste, and support uninterrupted production schedules.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: IPCF predictive analytics model focuses on forecasting operational inputs (ink and paper), which are supporting resources. While useful for inventory planning, the model does not directly influence strategic decisions or mission-critical outcomes.
US federal AI use case: Inventory Replenishment Forecast
Agency: Department of the Treasury - Bureau of Engraving and Printing (BEP)
Problem addressed: This model helps reduce supply chain disruptions by forecasting vendor performance and item stockouts. It enables proactive sourcing decisions, improves inventory planning, and strengthens operational readiness.
Purpose and benefits: Inventory Replenishment Forecast model helps identify underperforming vendors and forecast item stockouts, enabling proactive sourcing, improved inventory planning, and reduced supply chain disruptions to support mission continuity.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Impact assessment: This use case is not high-impact because it offers limited strategic value and low decision leverage across the enterprise. It proactively predict whether vendors will deliver their items by the date promised in the contract.
First used: 2023-09-15
US federal AI use case: IDV Model
Agency: Department of the Treasury - Bureau of Engraving and Printing (BEP)
Problem addressed: This model helps forecast long-term inventory turnover to improve IDV utilization and support proactive funding requests.
Purpose and benefits: IDV model helps forecast long-term inventory turnover, enabling smarter IDV utilization and proactive funding alignment to support sustained mission readiness.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Impact assessment: This use case is not high-impact because it centers on long-range forecasting with uncertain accuracy and limited influence on near-term procurement decisions or enterprise outcomes. It predicts which items will require IDVs for purchasing over the next 5 years by tier level will enable better utilization of IDVs overall and enable OSCM to proactively receive the necessary funding.
First used: 2025-04-02
US federal AI use case: Anomaly Detection for Data Quality
Agency: Commodity Futures Trading Commission - DOD
Problem addressed: Anomaly detection model designed to identify potentially erroneous data loads in TCR data. Uses an isolation forest model with aggregated data and automatically runs daily.
Development stage: Deployed
US federal AI use case: Stress Testing Scenarios with Deep Learning
Agency: Commodity Futures Trading Commission - DCR
Problem addressed: Pilot project to explore neural-network based machine learning methods for creating stress testing scenarios and estimating PnL (profit and loss) on FO (Futures and Options) portfolios based on the current/recent market states/conditions.
Development stage: Pre-deployment
US federal AI use case: MPD Entity Risk Modeling
Agency: Commodity Futures Trading Commission - MPD
Problem addressed: Entity-level risk modeling project. Use statistical and probabalistic models to predict firms experiencing changes in capital levels. Very early stage R&D effort.
Development stage: Pilot
US federal AI use case: The Advanced Trade Analytics Platform (ATAP)
Agency: Department of Homeland Security - CBP
Problem addressed: ATAP aims to provide insights into trends and behaviors in trade activity to support a proactive risk management and enforcement posture in the agency’s mission execution.
Purpose and benefits: To create efficiencies and unlock key insights in CBP's trade mission execution through the application of data analytics, machine learning, and AI.
Topic area: International Affairs
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI outputs do not produce an action or serve as a principal basis for a decision that has the potential to significantly impact the safety of human-life or well-being, climate or environment, critical infrastructure, or strategic assets or resources. Additionally, the AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services.
First used: 2022-03-07
US federal AI use case: Critical Infrastructure Network Anomaly Detection
Agency: Department of Homeland Security - CISA
Problem addressed: CyberSentry currently ingests hundreds of terabytes of data from Critical Infrastructure Partners every single day. At petabyte-scale over all collected network data, CyberSentry required a way to filter through the noise to be able to detect Advanced Persistent Threat (APT) and Nation State malicious activity happening within our Partners' networks. CyberSentry has developed numerous machine learning-based detections to identify trends, patterns, and anomalies in network data that ultimately result in both automated and manual triage by analysts.
Purpose and benefits: This use case delivers improved internal government tools for hunting and detection of malicious threat actors on critical infrastructure networks. It automates manual data fusion and correlation processes and highlights potential anomalies, allowing CISA analysts to focus more  time on hunting adversaries.
Topic area: Cybersecurity
Development stage: Deployed
First used: 2022-01-10
US federal AI use case: Malware Reverse Engineering
Agency: Department of Homeland Security - CISA
Problem addressed: This AI capability uses deep learning to assist CISA analysts with understanding the content of malware samples, automating tasks such as triage and indicator extraction.
Purpose and benefits: This use case delivers improved internal government tools for reverse engineering of malware and speeding the development of cyber threat intelligence that can be shared across the government and with CISA partners.  Threat actors can leverage the same malware for long periods of time, so having the ability to improve analysis and generation of shareable cyber threat intelligence forces threat actors to spend more resources generating new malware. Machine learning and other analytical tools are leveraged to guide malware analysts and automate elements of the manual reverse engineering process. Automation of tasks such as triage and indicator extraction allow threat hunting analysts to meet high demand and focus more on adversary response.
Topic area: Cybersecurity
Development stage: Pre-deployment
US federal AI use case: Voice Analytics for Investigative Data
Agency: Department of Homeland Security - ICE
Development stage: Retired
Risk category: Presumed high-impact (determined not high)
Impact assessment: The definition states that the "output serves as a principal basis for a decision or action concerning a specific individual or entity..."  However, the output of the AI is never the only basis for a decision, and no rights impacting action is taken unless a human is in the loop making a further determatination
US federal AI use case: Investigative Prioritization Aggregator
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of overwhelming data volumes that make it difficult for HSI personnel to prioritize high-value targets in criminal investigations.
Purpose and benefits: The sheer volume of data associated with investigations often overwhelms human capabilities, making it challenging for HSI personnel to analyze evidence and identify key players in criminal networks. Currently, there is no effective mechanism to quantify the level of evidence related to a particular subject or entity, or to determine which actors within a network are the most influential. This is particularly critical in the context of the counter-opioid/fentanyl mission, where timely and accurate intelligence is essential.

To address this challenge, this project utilizes machine learning to assign point values to data, enabling the scoring of information associated with a given selector, such as a phone number or legal name. This scoring system helps to understand the importance of an entity to investigations and the potential consequences of removing or neutralizing that entity. By doing so, HSI personnel can focus on high-priority targets and associated criminal networks, ultimately enhancing their ability to disrupt and dismantle these threats.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case provides more efficient data processing for HSI personnel. The AI output may be used to produce investigative insights in the form of data, information leads or connections that HSI personnel can use to inform investigations, but the output itself is data preparation and organization so HSI personnel can produce those leads when combining the AI output with the personnel’s expertise and other relevant investigative data and information. Personnel may use these insights for law enforcement purposes in ongoing investigations with existing targets to assist in activities such as producing risk assessments about individuals or identifying criminal suspects; however, all insights are reviewed and validated by both personnel and supervisors before being included in any official case management system, and do not serve as a principal basis for law enforcement action or decision. Any enforcement decisions related to these insights are outcomes of the full Federal Investigation Process involving verifying any insights as evidence (including validating AI-translated material by a certified interpreter), presentation to a U.S. Attorney’s Office and potentially a District Court judge, decision to prosecute, judicial review, and trial and sentencing.
First used: 2024-02-01
US federal AI use case: Text Analytics Data Science Sentence Similarity Model
Agency: Department of Homeland Security - USCIS
Problem addressed: The Text Analytics capability employs machine learning and data graphing techniques to identify patterns that may indicate potential fraud, national security, and/or public safety concerns by scanning the digitized narrative sections of the associated applications and looking for common language patterns.
Purpose and benefits: Text Analytics augments the tedious and time-consuming manual process to identify potential fraud, national security, and/or public safety concerns and enables the identification of such concerns across jurisdictional boundaries. It increases the integrity of immigration programs, strengthens officers’ confidence in their work, and contributes to the reduction in customer wait times.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2019-11-01
US federal AI use case: Automated Target Recognition (ATR) Developments for Standard Screening
Agency: Department of Homeland Security - TSA
Problem addressed: AIT systems need to use Automated Target Recognition algorithms to detect objects while maintaining passenger privacy.
Purpose and benefits: The purpose of this use case is to improve upon Automated Target Recognition (ATR) algorithms used to reduce privacy concerns because a TSO is no longer required to view Advanced Imaging Technology (AIT) images. The expected benefits are to increase detection, reduce false alarms, and improve efficiency and passenger experience.
Topic area: Transportation
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Accessible Property Screening (APS) Checkpoint CT Prohibited Items (PI) Detection
Agency: Department of Homeland Security - TSA
Problem addressed: The AI solution is needed to look for and classify non-explosive prohibited items, because this AI solution alongside the legacy explosive algorithms provides a complete solution for TSA. AI models can process vast amounts of data in real-time, identify anomalies, and provide a way to quickly evolve in identifying new threats with a speed and accuracy that humans cannot match. Once this AI solution is tested in the lab and in the field, TSA will have the capability of using Image on Alarm Only to enable TSOs to bolster accuracy while focusing on human-centered priority tasks.
Purpose and benefits: This AI helps airplane luggage checks by the TSA officers scanning bags to have a continuous always watching partner to alert them to anything suspicious. 

Currently, a Transportation Security Officer (TSO) who is assigned to every X-ray equipment at an airport checkpoint visually inspects each image. This officer resolves the system generated explosive alarms as well as visually inspecting the image for the presence of non-explosive prohibited items such as guns and sharp objects (see TSA Travel site).
TSA is working on developing new Artificial Intelligence/Machine Learning (AI/ML) algorithms to automate the search for the non-explosive prohibited items (e.g. guns, knives, etc.).  Once a threat is found, the algorithm displays bounding boxes around the suspect item for the operator to then investigate and adjudicate.  
These AI solutions benefit the public by providing a consistent and uninterrupted level of threat detection as an added layer of security. The ML algorithms allow the TSA officers to be more flexible and to better prioritize their attention on important items to improve security.
Topic area: Transportation
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Walk-Through Metal Detector (WTMD) Alternative Automated Target Recognition (ATR) Developments
Agency: Department of Homeland Security - TSA
Problem addressed: Improve detection over traditional WTMD to include non-metallic threats.
Purpose and benefits: Artificial intelligence (AI)-enhanced Millimeter wave (mmWave) detectors are used as an alternative to Walk-Through Metal Detectors (WTMDs) for passenger screening to detect both metallic and non-metallic threats and prohibited items on passengers at the security checkpoint. These detectors will provide both increased security and a better passenger experience.
Topic area: Transportation
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Synthetic data for improved Automated Threat Recognition (ATR) in checkpoint screening
Agency: Department of Homeland Security - TSA
Problem addressed: Images are used to train Automated Threat Recognition (ATR) and other AI models and systems to detect prohibited items in the screening processes.
Purpose and benefits: To create synthetic data that can be used to improve Automated Threat Recognition (ATR) algorithm development. Synthetic data can be quicker to produce which will improve effectiveness by addressing and adapting to new threats quicker.

Accessible Property Screening (APS) and On-Person Screening (OPS) are working with vendors and evaluating AI-based synthetic data generation techniques to bolster the pool of training data available to develop machine learning algorithms in ATR applications.
Topic area: Transportation
Development stage: Pre-deployment
Risk category: High-impact