GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Insider Threat Management and User Activity Monitoring
Agency: Department of Justice - Department of Justice / EOUSA
Problem addressed: Identification and prevention of anomalous user behavior too subtle for human detection and analysis
Purpose and benefits: Usage of this system proactively identifies and prevents costly data breaches, reducing incident response time, and gives EOUSA and the USAO comprehensive insight of user behavior to maintain security and compliance
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-06-01
US federal AI use case: Object Classification Tool - Field Office Security Camera
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Object anomaly detection
Purpose and benefits: Enhanced security
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: 2025-04-01
US federal AI use case: Data Synthesis, Sentiment, Filtering, and Location Linking
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Data triage and analysis
Purpose and benefits: Enhanced threat information detection
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2022-03-01
US federal AI use case: Facial Recognition Technology
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Facial recognition
Purpose and benefits: Generation of investigative leads
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-01
US federal AI use case: OCR and Translation
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Transcription and translation
Purpose and benefits: The AI will help the FBI digitize data.
Topic area: Law Enforcement
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: 2025-09-01
US federal AI use case: License Plate Reader 1
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: License plate reader technology assists in locating vehicles associated with persons of interest.
Purpose and benefits: Program uses character recognition to read and identify license plates.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Enterprise Telecommunications Information System
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Speech to text.
Purpose and benefits: Reduced customer wait time.
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: 2024-04-01
US federal AI use case: Facial Recognition Technology and Data Mapping Tools
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Facial recognition of open-source images
Purpose and benefits: Generation of investigative leads
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-09-01
US federal AI use case: Human Language Extraction and Translation Technology
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Machine translation of  documents and digital files
Purpose and benefits: Faster FBI operations
Topic area: Law Enforcement
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: 2025-05-01
US federal AI use case: Attrition and Background Models Capability
Agency: Department of Justice - Department of Justice / FBI
Development stage: Retired
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: Audio and Video Recording Management Tool
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Prevents loss of required audio and video data within FBI custodial interview rooms
Purpose and benefits: Tool facilitates audio and video recording within FBI custodial interview rooms, consistent with DOJ and FBI policy.
Topic area: Law Enforcement
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: License Plate Reader 2
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: License plate reader technology assists in locating vehicles associated with persons of interest.
Purpose and benefits: Program uses character recognition to read and identify license plates.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: License Plate Reader 3
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: License plate reader technology assists in locating vehicles associated with persons of interest.
Purpose and benefits: Program uses character recognition to read and identify license plates.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: National Crime Information Center
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Name matching
Purpose and benefits: The models can produce much quicker searches on word embeddings than they would otherwise be able to do, and can generate a larger return of similar phrases or misspellings.
Topic area: Law Enforcement
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: National Data Exchange
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Helps uncover valuable insights and connections in the text of criminal justice and law enforcement related information in the system.
Purpose and benefits: The benefit of this use case is to provide an entity extraction feature to aid N-DEx searches so that users can accurately search for people and filter the search results based on a specified role type.
Topic area: Law Enforcement
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: 2025-06-01
US federal AI use case: National Instant Criminal Background Check System
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Time-intensive review of database search results
Purpose and benefits: Improved quality of search results for NICS analysts when they examine state law databases for laws relevant to a NICS background check. More effective searches on word embeddings will better identify patterns in the data.
Topic area: Service Delivery
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: Next Generation Identification
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: The AI is intended to improve biometric and name-based matching for identification and investigation services.
Purpose and benefits: The AI provides more accurate biometric and name-based matching
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-06-01
US federal AI use case: TIPS
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: It is intended to prioritize tips to be worked as well as determine if a tip should get a second human review.
Purpose and benefits: The AI used in this case helps to triage immediate threats in order to help FBI field offices.
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: 2025-09-01
US federal AI use case: Facial Recognition Technology and Data Mapping Tools - 2
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Identification of sex trafficking victims
Purpose and benefits: Generation of investigative leads
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-01
US federal AI use case: Language Translation,  Optical Character Recognition (OCR), Object Detection, Language Detection, Alert Noise Reduction
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Autonomous Detection and Monitoring
Purpose and benefits: Increased analytic capability
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2021-01-01
US federal AI use case: ASCVD (Atherosclerotic Cardiovascular Disease) Risk Estimator
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: The ASCVD is used as a preventative measure to identify inmates that are at risk for heart disease in order to provide more aggressive treatment to reduce that risk.
Purpose and benefits: The expected outcome would be to have less heart disease or related complications due to the proactive assessment of potential risk.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
First used: 2023-08-01
US federal AI use case: Automated Medication Dispensing Cabinet
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: To log into the system faster and more securely by utilizing a fingerprint scanner.
Purpose and benefits: To prevent unauthorized access to the medications held in the cabinet.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
First used: 2000-03-01
US federal AI use case: Automated Staffing Tool
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: It will assess staffing levels within the FBOP.
Purpose and benefits: The expected benefit of the AI use case is to maximize cost effectiveness of staffing needs for each institution.
Topic area: HR
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: 2024-10-01
US federal AI use case: Aztec Learning Software
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Provide learners with data driven course work in Aztec LMS.
Purpose and benefits: Prediction:  Reports of trends, new authentic.
Topic area: Service Delivery
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: BRAVO Classification
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Uses statistical techniques to predict potential for misconduct for newly admitted inmates. In turn, this prediction is used to assign appropriate security levels.
Purpose and benefits: Correctly classifying inmates' security level will decrease the level of misconduct towards other inmates and staff.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2020-01-01
US federal AI use case: Building Automation Systems
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: An automatic control of a building's HVAC lighting and other systems through a centralized building management system.
Purpose and benefits: Reduce energy consumption and waste, monitor performance, and alerts for device failures.
Topic area: Admin Functions
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Community Treatment Pipeline Screening
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: This is an FBOP-developed tool to identify individuals due to be released to the community who require clinical review for additional treatment services once released into pre-release confinement.
Purpose and benefits: Reduces clinical reviews by a third by prescreening those with no SENTRY or BEMR indicators necessitating potential treatment needs.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
First used: 2017-02-01
US federal AI use case: Descript
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: AI synchronizes audio files to video
Purpose and benefits: Video to be used for communications and training purposes
Topic area: Service Delivery
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: 2024-09-01
US federal AI use case: Google Translate
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: FBOP employees have periodically used Google Translate to translate documents and notifications for the Spanish speaking population from English to Spanish.
Purpose and benefits: Translating documents from English to Spanish allows for the Spanish speaking population the opportunity to stay well informed with the events and details of the institution.
Topic area: Admin Functions
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: InterQual
Agency: Department of Justice - Department of Justice / FBOP
Development stage: Retired
Risk category: High-impact
US federal AI use case: Medical Claims Adjudication
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Uses Quantum Choice (QC) to adjudicate medical claims and analyze the data within the system.
Purpose and benefits: Cost savings and ensure compliance with billing regulations and contract pricing terms.  It also provides data analysis to assist with the FBOP's mission.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
First used: 2020-09-01
US federal AI use case: Medical Designations Calculator
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Initial medical and mental health care levels.
Purpose and benefits: Reducing the time to determine a final medical and mental health care level.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
First used: 2010-06-01
US federal AI use case: NLETs Arrest Classification
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Uses statistical techniques to classify text descriptions of arrests
Purpose and benefits: Correctly classifying arrest records to inform recidivism assessments
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2019-01-01
US federal AI use case: Pathfinder
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: To assist FBOP employees with career pathways.
Purpose and benefits: The system generates assessments based on user input.  It scores the assessments taken to provide a user with options for career pathways.
Topic area: HR
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: 2023-09-01
US federal AI use case: Prisoner Assessment Tool Targeting Estimated Risk and Needs (PATTERN)
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: The tool uses pre-defined rules to score an inmate's recidivism risk level.
Purpose and benefits: The tool provides the FBOP a recidivism risk instrument which objectively assesses an inmate's current level of risk for re-offending.
Topic area: Law Enforcement
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: 2021-08-01
US federal AI use case: Prisoner Assessment Tool Targeting Estimated Risk and Needs (PATTERN)
Agency: Department of Justice - Department of Justice / OJP
Problem addressed: The intended use is to predict the risk of recidivism for incarcerated adults.
Purpose and benefits: Addressing the risk of recidivism with appropriate programming and services to reduce the likelihood of reengagement with the justice system.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-07-01
US federal AI use case: Psychological Test Interpretation - Pearson Assessments
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Psychological test interpretation assistance.
Purpose and benefits: Assistance during psychological evaluations and treatment planning.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
First used: 2020-01-01
US federal AI use case: reCAPTCHA
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Uses reCAPTCHA to distinguish between a human and bot request.
Purpose and benefits: It provides verification and security to our websites.
Topic area: Law Enforcement
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: 2014-03-01
US federal AI use case: Static-99 Sex Offender Data System (SODS)
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Static-99 is used as one data point in a 10 point manual process to evaluate FBOP sex offender's risk for recidivism.  The tool uses pre-defined rules to score an inmate's recidivism risk level.
Purpose and benefits: To assist the evaluator in the recidivism risk review process by providing a key data point used in the overall evaluation.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2006-12-01
US federal AI use case: The R Project for Statistical Computing
Agency: Department of Justice - Department of Justice / FBOP
Development stage: Retired
Risk category: High-impact
US federal AI use case: Thomson Reuters Drafting Assistant Tool for MS Word
Agency: Department of Justice - Department of Justice / FBOP
Development stage: Retired
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: TruNarc, Smith Detection
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: This AI will test suspected mail for narcotics.
Purpose and benefits: It is utilized to confirm staff visual identification of narcotic through institution mail.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Trunet Systems
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Uses biometric (fingerprints and vocal) identification.
Purpose and benefits: It is an additional security feature that allows only that particular adult in custody (AIC) access to their individual account.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2001-08-01
US federal AI use case: Truview
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Analysis of data held within the TRU, visiting, case management, and volunteer systems.
Purpose and benefits: To assist with the FBOP's mission by completing assessments and analysis of the data input into the TRU, visiting, case management, and volunteer systems.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2007-03-01
US federal AI use case: UAS Threat Detector
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Detects and analyzes unmanned aerial systems (UAS) near FBOP facilities to determine if the UAS is a threat to an institution. Provides reliable threat detection as part of FBOP’s overall mission to provide safe and secure facilities.
Purpose and benefits: To maintain security of FBOP institutions. The benefits of such detections serve FBOP’s mission to protect society by confining offenders in the controlled environments of prisons and community-based facilities that are safe and appropriately secure.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2023-09-01
US federal AI use case: UpToDate
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: This tool is used for research for clinical practice guidance.  The user can submit a condition and the tool compiles a list of treatments and information that is currently being recommended in the medical field.
Purpose and benefits: Improved Patient Outcomes
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Veritone
Agency: Department of Justice - Department of Justice / FBOP
Development stage: Retired
Risk category: High-impact
US federal AI use case: Wellsaid
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: AI converts text to speech audio that is used in social media, training videos, and audiobooks.
Purpose and benefits: Audio to be used for communications and training purposes
Topic area: Service Delivery
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: 2024-09-01
US federal AI use case: Perimeter Detection Fence (FLIR)
Agency: Department of Justice - Department of Justice / FBOP
Development stage: Retired
Risk category: High-impact
US federal AI use case: Exiger Supply Chain Risk Management - DDIQ Research Engine
Agency: Department of Justice - Department of Justice / JMD
Problem addressed: Provides DOJ with capabilities to perform Supply Chain Risk Management assessments to support agency cybersecurity posture.
Purpose and benefits: Provides DOJ vendor profiles that aggregate data from open source repositories using API keys. This data gives DOJ information to make an informed decisions on whether or not to move forward with the acquisition of a goods/services based on the risks identified during research.
Topic area: Cybersecurity
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: 2023-09-01
US federal AI use case: Exiger Supply Chain Risk Management - DDIQ Due Diligence Analytics
Agency: Department of Justice - Department of Justice / JMD
Problem addressed: Provides DOJ with capabilities to perform Supply Chain Risk Management assessments to support agency cybersecurity posture.
Purpose and benefits: Provides DOJ with  analytical risk dashboards and company cybersecurity scorecards which provides DOJ with capabilities to make an informed decisions on whether or not to move forward with the acquisition of a goods/services based on the risks identified during research.
Topic area: Cybersecurity
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: 2023-09-01
US federal AI use case: CoPilot
Agency: Department of Justice - Department of Justice / Department wide
Problem addressed: Features assist with calendaring, meeting summaries, and email drafting.
Purpose and benefits: Reduces administrative time for user.
Topic area: Admin Functions
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: Savan Group Intelligent Records Consolidation Tool
Agency: Department of Justice - Department of Justice / JMD
Development stage: Retired
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: Salesforce
Agency: Department of Justice - Department of Justice / JMD / NSD
Problem addressed: Addresses the problem of complex data analysis and decision making by providing AI-driven capabilities that simplify and accelerate the process.
Purpose and benefits: Purpose: Provides contextual insights and AI-powered predictions and insights to drive engagement and focus directly in the flow of work in Salesforce. This license also includes AI features, which are not in use at this time.  Expected benefits: NSD procured CRM Analytics Plus licenses for the use of Tableau for reporting.
Topic area: Law Enforcement
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: Enhanced Proactive Financial Analysis Techniques for Fusion Desktop
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Deploys predictive analytics to enhance recognition of, and highlighting of, fraudulent financial activity across HSTF NCC financial and criminal holdings.
Purpose and benefits: Improve efficiencies by identifying and prioritizing financial fraud activity tied to open criminal investigations by including a broader data set and across multiple judicial districts to develop a better whole-of-US picture of financial fraud.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: FinCEN Data Summarization
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Analyze a SAR through generative AI functionality
Purpose and benefits: Speed up production times for providing investigative support to the field. It also makes analysts more available to perform deeper analysis.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Generation of Graphs and Charts
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Create charts and graphs by describing the requested data to integrate.
Purpose and benefits: Charts and graphs aide in reporting of HSTF investigations. Enabling this technology will reduce tremendously reduce the time which was used for formatting traditional Microsoft products.
Topic area: Law Enforcement
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: Generation of Large Test Data Sets
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Using generative AI modeling, we will create large volumes of test data so that quality assurance, external audits and penetration testing, system demos & integration, etc., can use that generated test data at scale.
Purpose and benefits: Developing and operating with a test set of data will enable engagements between HSTF and external groups, where manual generation of test data at scale  is not feasible.
Topic area: Law Enforcement
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: Machine Learning for Decision Support for Fusion Desktop
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: To take into account various criteria such as analyst workloads, similarities in case narratives and targets, and regional factors to suggest the optimal routing and assignment for product requests.
Purpose and benefits: Improve efficiencies in routing request for HSTF Products to send to the analysts and/or analytic units most suitable for working the product. Reduce rework and streamline the approvals process by suggesting agency approvers based on automated review of the product's content and ensuring all appropriate agencies are consulted for manual review.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Machine Learning for Decision Support for DOJ MIS
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Identify proposed HSTF investigations for prioritized review, approval, and potential case designation based on pre-determined success criteria.
Purpose and benefits: Improve efficiencies in routing HSTF proposals through the workflow to receive HSTF designation. Identify the best use of resources through machine learning trained on investigations matching emerging threats.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Narrative Analysis for Fusion Desktop
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Will create summaries or precis from large volumes of text narratives submitted on Fusions Desktop forms, for both information triage and trend analysis / emerging threat detection purposes. Gen AI would also allow interactive queries (e.g. you ask it questions) about the data it is summarized, and would be transparent (with citations of underlying data as needed).
Purpose and benefits: Provide rapid review of information submitted as data ingested into the Fusion Desktop database. This will reduce the time necessary during data entry and review and will provide guidance on emerging threat areas which HSTF NCC may use to determine resource allocation or areas for additional study.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Narrative Analysis for DOJ MIS
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Will create summaries or precis from large volumes of text narratives submitted on HSTF forms, for both information triage and trend analysis / emerging threat detection purposes. Gen AI would also allow interactive queries (e.g. you ask it questions) about the data it has summarized, and would be transparent (with citations of underlying data as needed).
Purpose and benefits: Provide rapid review of information submitted on DOJ MIS forms, including Investigation Initiation Forms (IIFs) and interim and final updates on open investigations. This will reduce the time necessary during data entry and review for forms and will provide guidance on emerging threat areas which HSTF NCC may use to determine resource allocation or areas for additional study.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Train ML on Intelligence Analyst Best Practices
Agency: Department of Justice - Department of Justice / JMD / OCDETF
Problem addressed: Train AI on exemplary analyst work to suggest course of action to all IAs while they perform product research and development.
Purpose and benefits: Improve efficiencies in developing HSTF NCC Products by providing decision support and limited automation by ML trained on exemplar analysts at the NCC. Provides guidance and suggestions particularly for resource-intensive actions, like conducting open source, commercial, or offline (swivel-chair) searches of data sets outside the NCC.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Speech to Text Managed Service - Voice Transcription to Text
Agency: Department of Justice - Department of Justice / OIG
Problem addressed: Reducing turnaround times for transcribed services and to reduce the need for contracted transcription services of audio files
Purpose and benefits: Cost savings towards less time and materials for an individual to fully process the recording and quicker turnaround of transcription delivery.
Topic area: Law Enforcement
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: 2024-04-01
US federal AI use case: Inspection Productivity Tool
Agency: Department of Justice - Department of Justice / OIG
Problem addressed: Reduce the time involved in analyzing draft reports and content recommendations in compliance with published standards, guidance, and rule books.
Purpose and benefits: Quicker turn around for creating reports, analysis of reports to ensure consistency in content and removal of redundancy, and to assist in the management of report length.
Topic area: Admin Functions
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Internal Component-specific chatbot service
Agency: Department of Justice - Department of Justice / OIG
Problem addressed: Provide human-like conversational responses to conduct general information queries and suggestions towards improving text without feeding into a commercially exposed AI model.
Purpose and benefits: Improve work efficiencies and curve user generative AI usage to an environment that is controlled and contained within GCCH and the DOJ OIG subscription vice a commercial service.  Furthermore, the information and models generated within the GCCH environment stays within GCCH and do not further the model of commercial products or services.
Topic area: IT
Development stage: Pilot
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: 2025-04-01
US federal AI use case: Dragon
Agency: Department of Justice - Department of Justice / OIG
Problem addressed: Automated word processing dictation and speech transcription.  The application is speech recognition software that initiates commands on a device or takes dictation into a word processing application.  The application assists individuals with reasonable accommodations related to and/or difficulty typing, seeing, or navigating a Windows operating system environment.
Purpose and benefits: Provide reasonable accommodations related to and/or difficulty with typing, seeing, or navigating a Windows operating system environment to be productive throughout the workday.
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: 2015-01-01
US federal AI use case: Informatica - CLAIRE
Agency: Department of Justice - Department of Justice / OIG
Problem addressed: Manual scanning and cataloging of data sets related to data analytics' mass data correction, business rules translation, data/column similarity, data anomaly detection, data relationship inference, data domain inference, data volume projections, cost of data breach, natural language description of code, business term associations, schema mapping, entity extraction, smart data visualization, and economic value of data.
Purpose and benefits: Informatica CLAIRE engine will help with cataloging enterprise data quickly, classify and organize OIG data. It will also automate the data curation and connect data across OIG from disparate sources.  It will also track data movement from system views to column-level lineage.
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: 2019-06-01
US federal AI use case: Nlets Nationwide License Plate Reader Pointer Index
Agency: Department of Justice - Department of Justice / OIG
Problem addressed: Law enforcement usage of Optical Character Recognition (OCR) to assist with license plate reading under DOJ Policy.  LPR data is managed and maintained by other entities for law enforcement purposes.
Purpose and benefits: Quick notification of results from license plate queries.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2019-10-01
US federal AI use case: Axon
Agency: Department of Justice - Department of Justice / OIG
Problem addressed: No AI utilized.  But, the use case captures raw video and audio footage that are accessed only to the extent needed for prosecution or investigation purposes.  AI functionalities for analysis, voice transcription, and redaction are not utilized or relevant for our use.
Purpose and benefits: This use case does not utilize AI functions.  AI features are included in the product, but have not been installed and not relevant for our use.
Topic area: Law Enforcement
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: 2022-03-01
US federal AI use case: SAS Enterprise Miner - Grant risk assessment model
Agency: Department of Justice - Department of Justice / OIG
Development stage: Retired
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: MACO Project: Law Enforcement CAD Data Autocoder
Agency: Department of Justice - Department of Justice / OJP
Problem addressed: MACO is intended to solve the lack of standardization in Computer-Aided Dispatch (CAD) data across law enforcement agencies. CAD event descriptions are free-text, highly variable, and differ widely in terminology, structure, and coding practices. Because of this inconsistency, it is currently not possible to aggregate, compare, or analyze CAD data across jurisdictions at scale. MACO uses machine learning and language models to automatically classify raw CAD text into a standardized event taxonomy, enabling consistent analysis, cross-agency comparisons, and the development of national estimates of calls for service.
Purpose and benefits: The AI provides standardized classifications of CAD event text, allowing BJS and partner agencies to analyze police activity consistently across jurisdictions. For BJS, this enables the production of scalable, comparable national estimates of calls for service—filling a major data gap not addressed by traditional crime measures. For state and local agencies, the standardized schema improves internal organization of CAD data and supports regional or state-level comparisons of police workload and community needs. For the research community and the public, MACO expands understanding of how law enforcement resources are used, the types of events agencies respond to, and broader patterns of community demand for police services. Overall, the tool enhances data quality, improves analytic capacity, and supports evidence-based decision-making across the criminal justice ecosystem.
Topic area: Law Enforcement
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: Offense Text Auto-Coder (OTAC) - Automated offense coder from offense charge text strings used for the BJS National Pretrial Reporting Program
Agency: Department of Justice - Department of Justice / OJP
Problem addressed: The purpose of this tool is to improve description and comparability of offense charges across jurisdictions. When using justice administrative data from various jurisdictions (localities, states, and federal), the way offenses are described (i.e., the exact text strings used) vary greatly. For example, assault and battery may be spelled out, or abbreviated in novel ways such as A&B, A & B, A+B, A?B, battery & aslt, etc. This tool is used to facilitate grouping identical concepts under one common set of offense codes. The data that BJS makes available will be aggregated or deidentified.
Purpose and benefits: Improved comparisons of criminal justice data
Topic area: Law Enforcement
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: Rapid Offense Text Autocoder (ROTA) - Automated offense coder from offense charge text strings used for the BJS Criminal Cases in State Courts
Agency: Department of Justice - Department of Justice / OJP
Problem addressed: The purpose of this tool is to improve description and comparability of offense charges across jurisdictions. When using justice administrative data from various jurisdictions (localities, states, and federal), the way offenses are described (i.e., the exact text strings used) vary greatly. For example, assault and battery may be spelled out, or abbreviated in novel ways such as A&B, A & B, A+B, A?B, battery & aslt, etc. This tool is used to facilitate grouping identical concepts under one common set of offense codes. The data that BJS makes available will be aggregated.
Purpose and benefits: Improved comparisons and analysis of criminal justice data.
Topic area: Law Enforcement
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: 2024-08-01
US federal AI use case: Research Abstract Screening for CrimeSolutions
Agency: Department of Justice - Department of Justice / OJP
Development stage: Retired
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: Adobe
Agency: Department of Justice - Department of Justice / PAO
Problem addressed: DOJ needs to communicate effectively internally and externally. This tool, can support image production for communication purposes.
Purpose and benefits: Adobe has function that enables the generation of images. PAO does not use this function/feature.
Topic area: Admin Functions
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: Hootsuite
Agency: Department of Justice - Department of Justice / PAO
Problem addressed: Optimizes social media posting
Purpose and benefits: PAO uses Hootsuite to schedule and manage social media products. Hootsuite also includes AI-powered social listening features, but PAO does not use those features.

It can advise on when the best time to post would be for maximum engagement, which will help promote our message.
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: 2009-10-01
US federal AI use case: Veritone Digital Media Hub
Agency: Department of Justice - Department of Justice / PAO
Problem addressed: Expedites the identification and labeling of photos.
Purpose and benefits: The Veritone Digital Media Hub includes AI features that allow PAO to search our event photo databases and identify objects in those photo catalogs.
Increased efficiency of searches of archival images so that PAO can find and continue using assets.
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: Lexis Nexis (People Search)
Agency: Department of Justice - Department of Justice / PARDON
Problem addressed: Improve the accuracy and comprehensiveness of a clemency applicant's personal data.
Purpose and benefits: Allows the retrieval of personal data for an individual such as historical addresses.

Improve the accuracy and comprehensiveness of a clemency applicant's personal data.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Pacer Search
Agency: Department of Justice - Department of Justice / PARDON
Problem addressed: Validate identity
Purpose and benefits: The service provides more confidence that the correct person has been identified
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2020-01-01
US federal AI use case: Westlaw (People Search)
Agency: Department of Justice - Department of Justice / PARDON
Problem addressed: Improve the accuracy and comprehensiveness of a clemency applicant's personal data.
Purpose and benefits: Allows the retrieval of personal data for an individual such as historical addresses.

Improve the accuracy and comprehensiveness of a clemency applicant's personal data.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01