GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: AI Cloud Environments
Agency: Department of Justice - Department of Justice / Department wide
Problem addressed: FedRAMP authorized environments are used to deploy tools that enable data-driven decision-making.
Purpose and benefits: To support expedited data collaboration and analytics.
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: AI CLIN Generation
Agency: Department of Justice - Department of Justice / JMD
Problem addressed: Determine the number of contract lines needed to meet contract requirements, outlines each line, and populates certain data for review within Unified Financial Management System.
Purpose and benefits: Reduce the time and effort of manual work to create contracts within Unified Financial Management System.
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: AI Chatbot
Agency: Department of Justice - Department of Justice / FBI
Problem addressed: Time intensive review of policy content
Purpose and benefits: Faster FBI operations
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: Adobe Premiere
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Seamless editing of videos and speech transcripts.
Purpose and benefits: The videos are for public-relations or educational, and not used for LE purposes.
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: 2025-03-01
US federal AI use case: Adobe Photoshop
Agency: Department of Justice - Department of Justice / FBOP
Problem addressed: Generative and image enhancement features, selection and workflow improvements.
Purpose and benefits: Better graphics and adjustments of photos for professional quality results.
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: 2025-03-01
US federal AI use case: Acquisition Support Tool
Agency: Department of Justice - Department of Justice / USMS
Problem addressed: The problem VAO Ally is intended to solve is the time and resource burden of navigating complex procurement regulations in an office that is already operating lean, even at near full staffing. . Ally quickly summarizes public-domain contracting regulations, simplifying technical language, and providing easy access to relevant rules and resources. This reduces the time staff spend searching for answers, minimizes the risk of misinterpretation, and allows contracting professionals to focus on the judgment-based, legally binding decisions that only they can make.
Purpose and benefits: The expected benefits of VAO Ally are faster access to accurate procurement guidance, reduced administrative burden on contracting professionals, and greater consistency in interpreting acquisition regulations. For the agency, this means improved efficiency in procurement processes, fewer delays caused by staff shortages or vacancies, and better use of limited resources to focus on mission-critical decision making. For the public, the outcome is a procurement workforce that can respond more quickly and effectively to agency needs, ultimately supporting timely delivery of government services and safeguarding taxpayer dollars.
Topic area: Procurement and Finance Mgmt
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: 2025-02-01
US federal AI use case: Simulate Regulatory Audits to Train Diversion Investigators and Improve Audit Protocols
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Enables simulations of DEA Diversion audits that can be used to train new Diversion Inspectors while evaluating DEA audit protocols for inconsistencies and vulnerabilities-to generate best practice models.
Purpose and benefits: Ultimately, this will improve the efficiency and quality of regulatory audits, which will increase the number of civil fines that we issue.
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: Parse and Capture Data Submitted by Laboratories to the NFLIS Program
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Collecting and entering information from registrants in the National Forensic Laboratory Information System (NFLIS) program takes considerable time and effort and is prone to human error
Purpose and benefits: Cost savings, reduced customer wait times, and improved accuracy of reporting.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Managing Document Digital Signatures
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: It is currently time-consuming, and challenging, to collect digital signatures from DOJ personnel into one source document. Utilizing AI would intend to manage the document workflow by using a single upgraded storage location with advanced security features.
Purpose and benefits: Saves time and effort by integrating identity verification while reducing the amount of storage required for documents.
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: Creating and Maintaining IT Security Packages for Authorizations
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Cybersecurity personnel want to automate the identification of security controls that can have implementation statements, create and maintain security packages for authorizations, keep up with compliance requirements, and more quickly onboard new systems and applications.
Purpose and benefits: Reduces costs while enabling use to maintain compliance and consistency
Topic area: Admin Functions
Development stage: Pre-deployment
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.
US federal AI use case: Community Outreach Chatbot that Helps the Public Consume Prevention Resources
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Many citizens may not have the time to explore the copious library of DEA Community Outreach and Prevention Support (CPO) resources. By training AI on these resources, citizens can ask plain language questions and get succinct answers.
Purpose and benefits: Increased usage of drug use prevention resources by citizens. Extends the reach of CPO into communities who have a high risk of drug abuse.
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: Chatbot to Answer Diversion Registrants' Queries About Registration and Compliance Issues
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: TOMSS (Technical Operations Management Support System  will provide call-deflection to ease the customer interaction burden on call center representatives, registration program specialists, and Diversion Investigators. The AI will provide consistent, accurate responses to repetitive queries and replace manual workloads.
Purpose and benefits: 30–40% reduction in repetitive calls and emails.
• 15–25% reduction in agent workload.
• 100% of chatbot responses sourced from verified DEA policy.
• 24/7 access to authoritative self-service guidance for registrants.
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: Capture and Convert Structured Data from Scanned Case Documents to Support Advanced Analysis and Trend Forecasting
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: The Targeting & Special Projects Unit (DOIT) has identified a critical need to capture, convert, and process both structured and unstructured text from scanned case documents to support advanced analysis and trend forecasting. This initiative will leverage an optical character recognition (OCR) solution capable of extracting both typed and handwritten content from diverse sources, including medical notes, invoices, and statements.
Captured text will be transformed into a standardized, machine-readable format (e.g., CSV) and integrated into a relational database. From there, advanced analytical techniques will be applied to reveal hidden structures, patterns, and relationships within the data. By unlocking this information, we aim to enhance our ability to anticipate trends, strengthen investigative strategies, and move toward a more predictive, data-driven approach
Purpose and benefits: Save valuable investigative time that can be used to focus on the results of the analysis. Conducting more comprehensive analysis on the data will improve trend forecasting, strengthen investigative strategies, and support a more predictive, data-driven approach.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Auditing Diversion Registrant Inventories of Controlled Substances
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Diversion Investigators regularly conduct accountability audits of Registrants' management of controlled substances in order to detect diversion. Registrants sometimes submit hundreds of pages of invoices, receipts, and logs, which must be reconciled against inventory and movement records to ensure that the audits balance. Diversion Investigators review these and mark them for review if there is non-compliance with regulations, such as missing information or missed signatures. This current workflow relies on manual data entry into spreadsheets, leading to errors, inconsistencies, and excessive investigative time.
Purpose and benefits: Reduces the amount of investigator time per audit by automating the extraction of key fields (e.g., item, quantity, cost, dates) from invoices and auto-populating computation charts.

Standardized extraction and reconciliation eliminates human entry errors and decreases audit reconciliation error rates.

Real-time flagging of discrepancies (e.g., inventory errors, excess sales, mismatched destruction records, possible fraudulent records) hastens the detection of potential diversion.

Frees investigators to focus on high-value investigative activities and field operations, saving thousands in costs.

Provides an auditable trail of extraction and reconciliation for internal and external audits. Maintains full audit-ready compliance for all registrants and a tangible audit trail for legal proceedings.
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: Analyzing Prescription Monitoring Program Data
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA investigators currently rely on manual manipulation of data in spreadsheets to conduct Prescription Monitoring Program (PMP) analysis. This requires 8-10 hours of labor (data extraction, cleaning, cross referencing, narrative synthesis), and, since investigators analyze data in different ways, results vary. There is little  consistency across the agency or even between investigators in terms of what data is examined and there is no easy way to identify patterns such as MMEs, combinations, and early fills.
Purpose and benefits: Increases operational efficiency by automating data preparation, risk identification, and initial narrative generation. Average time spent to produce a PMP decreased from ten to two hours (minus 80%).

By employing consistent, data-driven risk analysis, increases the proportion of high-value investigations that result in successful enforcement or administrative outcomes.

Reallocates Diversion Investigator effort to higher-value tasks (e.g., field operations, strategic planning), producing a labor cost benefit in the thousands per Diversion Investigator based on average pay and number of PMPs analyzed.

Faster identification of prescribing anomalies supports earlier public health interventions and reduces community exposure to diverted pharmaceuticals.

A single, centrally managed AI service can be provisioned to all DEA field offices, ensuring uniform analytic standards.
Topic area: Law Enforcement
Development stage: Pre-deployment
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.
US federal AI use case: Analyze Financial Reports to Identify Linkages Across Investigations
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Comprehensive analytical review of reports related to money laundering, specifically identifying commonalities across multiple investigations.
Purpose and benefits: Analysts no longer have to spend time manually collecting data from reports and importing it into PowerBI dashboards as a first step in linking investigations and showing a larger picture of criminal networks.
Topic area: Law Enforcement
Development stage: Pre-deployment
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.
US federal AI use case: Language Translation
Agency: Department of Labor - OCIO
Problem addressed: Eliminates language barriers by translating documents and communications across multiple languages.
Improves access to services and information for non-English-speaking stakeholders
Purpose and benefits: Quick translation to different languages  using natural language processing models.
Topic area: Other
Development stage: Pre-deployment
Impact assessment: This service only provides language translation and the translation will be review by human not used without human validation.
US federal AI use case: Audio Transcription
Agency: Department of Labor - OWCP||VETS||WHD
Problem addressed: The AI is intended to ensure accurate records of voicemails for tracking and follow-up purposes on claims.  The transcription service allows more time for the claims examiner to focus on more critical tasks such as decisions on claims as opposed to spending time documenting the call themselves.  Having more time to focus on critical tasks, in turn, enables higher levels of customer service.
Purpose and benefits: Transcription of speech to text for records keeping using natural language processing models.
Topic area: Other
Development stage: Deployed
Impact assessment: This service only provides transcription of calls. It does not serve as a principal basis for decisions or actions with significant effect on an individual or entity's civil rights, civil liberties, or privacy, access to critical government resources or services, or human health and safety.
First used: 2023-03-22
US federal AI use case: Text to Speech Conversion
Agency: Department of Labor - EBSA || OHR
Problem addressed: Text to speech (Neural) for more realistic human sounding applications using natural language processing models.
Purpose and benefits: Provide more realistic human sounding applications
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Used for audio output only; no automated decisions or sensitive data involved—low risk
US federal AI use case: Claims Document Processing
Agency: Department of Labor - OWCP
Development stage: Retired
Impact assessment: Data extraction only,,  but human makes the decision,Not pursuing in 2025; discontinued pre-deployment
US federal AI use case: Website Chatbot Assistant
Agency: Department of Labor - OTAA
Development stage: Retired
Impact assessment: Read-only, informational chatbot: draws from authoritative sources and case-status data, makes no determinations
US federal AI use case: Hololens
Agency: Department of Labor - OSHA
Problem addressed: AI used to train Inspectors to visually inspect  unsafe areas from a safe location.
Purpose and benefits: Vastly more cost effective in using the AI tool. Reduction of FTE costs from months to hours.
Topic area: HR
Development stage: Deployed
Impact assessment: Training and simulation only
First used: 2024-11-10
US federal AI use case: Determine Occupation Codes from Text (NIOCCS)
Agency: Department of Labor - OSHA
Development stage: Retired
Impact assessment: Does not involve sensitive data or direct impact on individuals’ rights or access to services, Not pursuing in 2025
US federal AI use case: Official Document Validation
Agency: Department of Labor - OWCP
Development stage: Retired
Impact assessment: Identification of an anomaly,Not pursuing in 2025; discontinued pre-deployment
US federal AI use case: Electronic Records Management
Agency: Department of Labor - OCIO
Problem addressed: Meeting NARA metadata standards for (permanent) federal documents by using AI to identify data within the document, and also using NLP to classify and summarize documents.
Purpose and benefits: Improved efficiency and mission effectiveness
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Only classfication and summarization for human review
US federal AI use case: Call Recording Analysis
Agency: Department of Labor - EBSA
Problem addressed: Automatic analysis of recorded calls made to Benefits Advisors in the DOL Interactive Voice Repsonse (IVR) center. AI is used for Trend analysis and Sentiment Analysis
Purpose and benefits: Provide transcripts of audio files
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Trend analysis and sentiment analysis only
US federal AI use case: Automatic Document Processing
Agency: Department of Labor - OWCP
Problem addressed: The AI is intended to allow the claims examiner more time to focus on more critical adjudication tasks.  The CM-929 and CM-908 forms amount to tens of thousands of incoming correspondence on an annual basis.  Claims examiners were cumulatively spending an excess amount of time each year reviewing the forms only for indications of a change in circumstances (based on a check-the-box indicator) or similar notice.  This would then be followed by the entry of a code signifying receipt in the Automated Support Package.  Only a very small number of forms received each year require a modification of benefits.  AI is easily able to handle this task, and ensures that forms indicating a change in circumstances are reviewed by a human, while the remainder are processed by the AI, reducing wait times in other areas of the adjudication process for claimants.
Purpose and benefits: Automatic processing of continuation of benefits form to extract pre-defined selection boxes. AI tool will extract data from the forms.
Topic area: Benefits Processing
Development stage: Deployed
Impact assessment: This service reads inputs on annual CM-929 and CM-908 forms received. It does not serve as a principal basis for decisions or actions with significant effect on an individual or entity's civil rights, civil liberties, or privacy, access to critical government resources or services, or human health and safety. Any form with unreadable inputs or responses that could result in a change in benefits are directed to a human reviewer for final review and action.
First used: 2023-05-29
US federal AI use case: Automatic Data Processing Workflow with Form Recognizer (9130/9178 Reconciliation Project)
Agency: Department of Labor - ETA
Development stage: Retired
Impact assessment: Only extracting data from forms,Not pursuing in 2025; discontinued pre-deployment
US federal AI use case: Generative AI AssistantI  ( AI Center)
Agency: Department of Labor - OCIO
Problem addressed: Private and secure in-house solution to evaluate business use cases that can solve problems using Generative AI models and semantic search. Example use cases include text summarization, text analysis, and document comparisons.
Purpose and benefits: Private, secure in-house generative AI with semantic search accelerates text summarization, analysis, and document comparison to improve efficiency and decision support.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Generating content, summarizing and reviewed by human
First used: 2024-09-01
US federal AI use case: Occupation Code Suggestion For Job Duties Data
Agency: Department of Labor - ETA
Development stage: Retired
Impact assessment: Code suggestions only; mandatory human review; no automated decisions, Not pursuing in 2025
US federal AI use case: Notice of Deficiency (NOD) Text Generation using Generative AI model
Agency: Department of Labor - ETA
Development stage: Retired
Impact assessment: Process automation only, Not pursuing in 2025; discontinued pre-deployment
US federal AI use case: Case Recording summarization
Agency: Department of Labor - OSHA
Development stage: Retired
Impact assessment: It does not involve sensitive data
It does not directly affect individuals’ rights or access
It is used for administrative summarization, not decision-making
US federal AI use case: OEWS Occupation Autocoder
Agency: Department of Labor - BLS
Problem addressed: The input is state-submitted response files that include occupation title and sometimes job description of the surveyed units. The autocoder reads the job title and assigns up to two 6-digit Standard Occupational Classification (SOC) codes along with their probabilities as recommendations for human coders. Codes above a certain threshold are appended to the submitted response file and sent back to states to assist them with their SOC code assignment.
Purpose and benefits: Improving data accuracy and saving time for SOC coders
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2018-01-01
US federal AI use case: Scanner Data Product Classification
Agency: Department of Labor - BLS
Problem addressed: BLS receives bulk data from some corporations related to the cost of goods they sell and services they provide. Consumer Price Index (CPI) staff have hand-coded a segment of the items in these data into Entry Level Item (ELI) codes. To accept and make use of these bulk data transfers at scale, BLS has begun to use machine learning to label data with ELI codes. The machine learning model takes as input word frequency counts from item descriptions. Logistic regression is then used to estimate the probability of each item being classified in each ELI category based on the word frequency categorizations. The highest probability category is selected for inclusion in the data. Any selected classifications that do not meet a certain probability threshold are flagged for human review. Benefits: real-time turnaround, productivity improvements, cost savings.
Purpose and benefits: Efficiency of item classification efforts
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2019-03-01
US federal AI use case: Expenditure Classification 
Autocoder
Agency: Department of Labor - BLS
Problem addressed: Assigns expense classification categories to reported expenses from Consumer Expenditure Diary Survey respondents.
Purpose and benefits: Efficiency of expenditure classification efforts
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2024-01-01
US federal AI use case: PII Redaction
Agency: Department of Labor - OSHA
Problem addressed: Using Amazon Web Services Personal Identifying Information scrubber for ITA text fields and Named Entity Recognition to remove additional names from the text fields.
Purpose and benefits: Vastly more cost effective in using the AI tool. Reduction of FTE costs from months to hours.
Topic area: Cybersecurity
Development stage: Deployed
Risk category: High-impact
Impact assessment: High Impact Use case
First used: 2024-11-19
US federal AI use case: Workforce Recruitment Program (WRP) Website Chatbot Assistant
Agency: Department of Labor - ODEP
Problem addressed: This use case cuts down on the number of emails that ODEP’s WRP team needs to respond to in their program inbox, wrp@dol.gov. It helps address routine questions with straightforward answers without requiring WRP staff involvement. It also provides much faster responses to WRP applicants and stakeholders, who would otherwise need to wait 2-3 business days for WRP staff to write a response to their email.
Purpose and benefits: The chatbot helps the end user with basic information about the Workforce Recruitment Program and information on who to contact. This chatbot is managed by DOL staff. ODEP manages the script, while OCIO manages the technical aspects and updates to Amazon Lex.
Topic area: Service Delivery
Development stage: Deployed
Impact assessment: Read-only, informational chatbot: draws from authoritative sources and case-status data, makes no determinations
First used: 2024-08-16
US federal AI use case: Initial Determinations for incoming applications
Agency: Department of Labor - ETA
Development stage: Retired
Impact assessment: Process automation,Not pursuing in 2025; discontinued pre-deployment
US federal AI use case: Web Scraping to Identify Potentially Fraudulent Multiple Employer Welfare Arrangements (MEWAs)
Agency: Department of Labor - EBSA
Development stage: Retired
Impact assessment: Not pursuing in 2025; discontinued pre-deployment
US federal AI use case: Worker PLUS Microsimulation Program
Agency: Department of Labor - CEO - WB
Problem addressed: The Worker PLUS model was developed as an evolutionary iteration of the Paid Family and Medical Leave 
Simulator Model developed by Albelda and Clayton-Matthews (the ACM model)
Purpose and benefits: Worker Paid Leave Usage Simulation (Worker PLUS) is an open-source simulation tool that can be used by researchers and federal, state, and local policy makers to estimate the effects of various worker leave scenarios and policy options on worker leave-taking behavior, and to estimate the benefits paid as well as costs of administering any given program.​
Topic area: HR
Development stage: Deployed
Impact assessment: Not making decisions only simulation
First used: 2021-03-01
US federal AI use case: Computer-Assisted Coding: 
SOII Autocoder
Agency: Department of Labor - BLS
Problem addressed: The Survey of Occupational Injuries and Illnesses (SOII) collects hundreds of thousands of narratives describing cases of work-related injury and illness annually. Using narratives and other relevant information, SOII Autocoder automatically assigns classifications for SOII elements, which include worker occupation, nature of the injury, part of body affected, event that resulted in the injury, source and secondary source (if it exists) that caused the injury. This process used to be performed entirely by human staff.
Purpose and benefits: Efficiency and data quality 
improvements
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2015-01-15
US federal AI use case: CFOI Record Matching
Agency: Department of Labor - BLS
Problem addressed: The Census of Fatal Occupational Injuries (CFOI) collects and publishes a complete count of work-related fatal injuries and descriptive data on their circumstances. The CFOI Record Matching programs matches records from various sources, including Occupational Safety and Health Administration (OSHA) Information System (OIS) files, news articles, and Quarterly Census of Employment and Wages (QCEW), to identify missing or inconsistent data in CFOI.
Purpose and benefits: Efficiency and data quality 
improvements
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2024-09-20
US federal AI use case: OIICS Coding
Agency: Department of Labor - OSHA
Development stage: Retired
Impact assessment: Statistical coding assistance; human review; no impact on rights or benefits. Not Pursuing in 2025
US federal AI use case: Note Taking Bot
Agency: Department of Labor - OCIO
Problem addressed: The bot will take the transcript and summarize the meeting notes
Purpose and benefits: An AI bot that converts meeting transcripts into concise, searchable notes improves productivity and information sharing by reducing manual effort.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Only summarizing the transcript
First used: 2024-11-12
US federal AI use case: CPS OTC Prediction
Agency: Department of Labor - BLS
Problem addressed: The BLS productivity office publishes measures of hours worked by major sector and industry. As part of the calculation for this measure, BLS uses Current Employment Statistics (CES) data on hours paid is used to estimate hours for payroll workers. Additional adjustments are made to this measure, such as removing paid time off (PTO), adding off-the-clock (OTC) hours, and adding in hours worked by self-employed and unpaid family workers. BLS uses the Current Population Survey (CPS) dataset to identify the amount of hours that are worked off-the-clock (OTC) by workers. There are some workers in the CPS dataset that do not report whether their time off was paid, or whether they get paid hourly or not. This data needs to be imputed to calculate the ratio of total hours worked to paid hours worked. A random forest model is used to predict the responses for workers that did not report this information by training it on characteristics (such as industry, occupation, education level, age, etc.) of respondents that have reported that information.
Purpose and benefits: Efficiency and data quality 
improvements
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2022-11-01
US federal AI use case: Sample Refinement: Frame API
Agency: Department of Labor - BLS
Problem addressed: BLS establishment-based surveys, such as the Survey of Occupational Injuries and Illnesses (SOII), the National Compensation Survey (NCS), and the Occupational Requirements Survey (ORS), use the Longitudinal Database (LDB) as the frame for their survey samples. For each of these programs, there is a significant time-lag between when the sample is drawn for a program and when data collection begins. A critical and time-consuming part of data collection is sample refinement: checking the latest Quarterly Census of Employment and Wages (QCEW) and LDB data to adjust for any changes to the sampled establishment during that time-lag. The QCEW/LDB Frame API allows fast retrieval of the latest QCEW/LDB data for a nationwide sample so that users can perform sample refinement more efficiently. The SOII program uses the Frame API to perform over 20 sample refinement comparison checks, which outputs a report for the data collectors.

The Frame API uses TF-IDF vectors and cosine similarity to compare company names, mailing addresses, and unit descriptions between the survey sample and the latest QCEW/LDB data to determine what components may have changed. The Frame API also has the flexibility for the users to match records using other combinations of variables.
Purpose and benefits: Efficiency and data quality 
improvements
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2024-09-20
US federal AI use case: CE Interview Item Code Estimation
Agency: Department of Labor - BLS
Problem addressed: Recommends expense classification categories for Consumer Expenditure Interview Survey expense responses.
Purpose and benefits: Improves data quality by identifying potentially incorrect misclassifications from data collection for human review
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2019-07-01
US federal AI use case: CE Interview Imputations
Agency: Department of Labor - BLS
Problem addressed: Imputes missing expenditure values in the Consumer Expenditure Interview Survey when respondents answer “don’t know” or refuse to provide an expense amount.
Purpose and benefits: Uses more sophisticated statistical techniques to estimate missing values
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for survey processing for statistical purposes
First used: 2020-10-01
US federal AI use case: QCEW NAICS Autocoder
Agency: Department of Labor - BLS
Problem addressed: The primary data source for the Quarterly Census of Employment and Wages (QCEW) is administrative data from state unemployment insurance programs. Some of these records are submitted without a valid North American Industry Classification System (NAICS) code.
Purpose and benefits: The tool is intended to reduce the burden on human reviewers of these uncoded records by recommending the most likely NAICS codes. It also reduces the burden on the program office by reducing printing and mailing costs of soliciting unclassified data.
Topic area: Other - Applied Mathematical Sciences
Development stage: Pilot
Impact assessment: Used for survey processing for statistical purposes
First used: 2025-06-01
US federal AI use case: Comment Actionability
Likelihood Score
Agency: Department of Labor - BLS
Problem addressed: Many comments submitted through the BLS.gov customer satisfaction survey are not actionable (i.e., do not provide an issue or explanation of what could improve the user experience). Non-actionable submissions increase the burden on those tasked with reviewing comments.
Purpose and benefits: The tool reduces the burden on those tasked with reviewing these comments by providing them with a means to sort them by how actionable they are likely to be.
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Used for customer feedback survey and assists staff with reviewing comments, does not replace human involvement
First used: 2024-09-01
US federal AI use case: Computer-Assisted Review:
ORS Autocoder
Agency: Department of Labor - BLS
Problem addressed: The Occupational Requirements Survey (ORS) collects and publishes information about specific work-related requirements. Every year, thousands of collected data must be reviewed manually by human staff. The ORS Autocoder, on a regular basis, autocodes and clears certain edit flags from records which it deems to be good, allowing human staff to focus on reviewing data that are more likely to be incorrect.
Purpose and benefits: Efficiency and data quality improvements
Topic area: Other - Applied Mathematical Sciences
Development stage: Pre-deployment
Impact assessment: Used for survey processing for statistical purposes
First used: 2025-05-28
US federal AI use case: PPI Price Tolerance Prediction
Agency: Department of Labor - BLS
Problem addressed: Recommends price tolerance boundaries for industries based on historical and seasonal trends.
Purpose and benefits: Improve statistical quality of price outlier detection.
Topic area: Other - Applied Mathematical Sciences
Development stage: Deployed
Impact assessment: Does not meet definition in Section 5 of the appendix to OMB Memorandum M-25-21.
First used: 2025-10-01
US federal AI use case: EBSA Case File Summarization
Agency: Department of Labor - EBSA
Problem addressed: Identify and describe electronic documents received by EBSA investigators for formal investigations - save time by automating the first phase of the document review process
Purpose and benefits: Save time for EBSA investigators by automating the first phase of the document review process
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: AI summarizes documents to assist investigators; humans conduct independent review and retain full investigative authority. Does not meet high-impact definition per M-25-21 Section 5."
US federal AI use case: AI Headset
Agency: Department of Labor - MSHA
Problem addressed: Inspection: An AI headset offers a hands-free, voice-controlled solution that enhances the efficiency, safety, and accuracy of MSHA inspectors using the Inspection Application System (IAS). By enabling inspectors to access inspection checklists, capture photos and videos, and input data through voice commands, the headset reduces reliance on manual data entry and handheld devices in mining environments. It also supports real-time communication with field office supervisors and data collection, streamlining the inspection process and improving documentation. When integrated with IAS or compatible platforms, an AI headset  can significantly modernize field inspections, ensuring better compliance and faster reporting.
Purpose and benefits: Improve Inspection Onsite Documentation Time: The intended purpose of the AI headset is to provide a hands-free, voice-activated wearable solution that enhances the productivity, safety, and effectiveness of frontline government workers, such as MSHA inspectors. The expected benefits include significant cost savings through reduced inspection times and fewer repeat site visits, as well as improved documentation accuracy through real-time photo, video, and voice note capture. The device also enables live communication with remote experts, accelerating decision-making and reducing downtime.
Topic area: Other
Development stage: Pilot
Impact assessment: AI headset provides voice-accessed information, checklists, regulations, and multimedia capture assistance only; inspectors retain full authority to determine violations and enforcement actions. Does not meet high-impact definition per M-25-21 Section 5."
First used: 2025-12-18
US federal AI use case: NLP Processing tool for ILAB
Agency: Department of Labor - CEO-ILAB
Problem addressed: The Natural Language Processing tool is intended to be a conversational query and synthesis tool that summarizes and synthesizes evaluation and technical assistance reports, as well as responses to requests for public comments, and queries information contained in these reports. The tool uses a Retrieval-Augmented Generation (RAG) approach and will include processing of both publicly available and restricted-access documents to train and test the tool.
Purpose and benefits: Rapidly summarizes and synthesizes large volumes of evaluation and technical assistance reports, thereby increasing efficiency
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Only query and returining answers
US federal AI use case: DAISI ( DOL AI Search Insights)
Agency: Department of Labor - OCIO
Problem addressed: It's an AI-powered search assistant that retrieves and summarizes content from internal DOL sources to assist with information discovery. DAISI generates responses based on a database of facts, with all data coming from intranet content.
Purpose and benefits: Faster retrieval of information
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Retrieving content from existing sources
First used: 2025-09-12
US federal AI use case: ETA Grants Monitoring Tool through Doc Explorer
Agency: Department of Labor - ETA
Problem addressed: Automates review of grant documents to detect anomalies, delays, and compliance issues.
Improves consistency and accuracy in monitoring across programs.
Purpose and benefits: Saves time during site visit. Enhances monitoring efficiency and ensures accurate, timely use of Grants
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Used to gather information from reports to make Grantees inspection easier
First used: 2025-07-05
US federal AI use case: USDOT Compliance Plan for OMB Memorandum M-24-10 (September 2024)
Agency: Department of Transportation - CAIO, NETT
Problem addressed: This Compliance Plan conveys DOT's approach to achieving consistency with OMB Memorandum M-24-10 Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence. The plan aligns with M-24-10's three main pillars of Strength
Purpose and benefits: Provides the Department with a roadmap for accelerating the responsible use of AI and complying with OMB directives.
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-07-25
US federal AI use case: Advanced Research and Testing (ART) Network
Agency: Department of Transportation - CAIO, Volpe, OST-R
Problem addressed: The ART Network is the shared service environment for AI research and development activities that provides researchers with access to a rapid AI innovation, exploration, development, and sharing platform using secure and approved IT infrastructure.
Purpose and benefits: The ART Network shared service accelerates AI-enabled research at the Department by providing researchers with direct access to an established platform that contains all available AI tools for that environment rather than making changes to an existing env
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-10-24
US federal AI use case: AI Operations Laboratory (OPSLAB)
Agency: Department of Transportation - CAIO
Problem addressed: Provides operational AI use case developers with access to shared environments with all OCIO-cleared AI functionality for use case experimentation, development, and initial data and model risk management identification and mitigation.
Purpose and benefits: The OPSLAB shared service accelerates AI-enabled operational use case development at the Department by providing AI developers  with direct access to an established platform that contains all available AI tools for that environment rather than making chan
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Transportation AI-enabled Network (TrAIN)
Agency: Department of Transportation - CAIO
Problem addressed: The TrAIN is the set of shared service environments for operational AI use cases that provides AI developers with a pre-established, secure AI-enabled environments for development, testing and deployment under the Chief AI Officer's compliance and risk ma
Purpose and benefits: The TrAIN shared service accelerates AI-enabled operational use case deployment at the Department by providing AI developers  with direct access to an established platform that contains all available AI tools for that environment rather than making change
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI Support and Collaboration Center (AISCC)
Agency: Department of Transportation - OIE, CAIO, OST-R, OCIO
Problem addressed: The AISCC provides all DOT employees and contractors with a website for getting educated on AI topics, get inspired by DOT and external use cases and capabilities, collaborate with internal and external AI communities of practice, partner with AI subject
Purpose and benefits: Provides the Department's north star for the delivery of AI education, inspiration, and direction to the workforce.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-10-09
US federal AI use case: Enterprise Personal Productivity Assistant Capability
Agency: Department of Transportation - CAIO, HASS, Volpe
Problem addressed: Secure enterprise AI-enabled document generative AI capabilities.
Purpose and benefits: Research and operating administrations are able to use application program interfaces (APIs) to develop employee productivity enhancing solutions for querying user-provided documents.
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Enterprise "Ask Dottie" ChatBot Capability
Agency: Department of Transportation - CAIO, HASS, Volpe
Problem addressed: Secure enterprise AI-enabled question and answer ChatBot capabilities for natural language querying of documents and data.
Purpose and benefits: Provides the Department with secure enterprise-wide LLM ChatBot capabilities that can be tailored to specific use cases.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Certified Professional Controller (CPC) On-Board Success Evaluator
Agency: Department of Transportation - FAA AJO
Problem addressed: CPC Outlook Generator (COG) is a front end Graphical User Interface built to interact with a data model called CPC Onboarding Success Evaluator (COSE), a machine learning model capable of predicting how many Certified Professional Controller (CPCs) the FA
Purpose and benefits: Based on what-if scenario, the system provides a graphical representation of a 5 or 10 year CPC staffing outlook (which is the predictive component provided by the ML).  Alongside with the cost (training and salary) the "when" the target shifts (is it ear
Topic area: Transportation
Development stage: Deployed
First used: 2023-02-10
US federal AI use case: Automated Delay detection using voice processing
Agency: Department of Transportation - FAA AJO
Problem addressed: AI-based voice recognition of Air Traffic Control (ATC) and pilot communications associated with a flight is required for enhanced and accurate delay reporting and attribution.  Many delay events, such as vectoring, are not currently reported/detected/acc
Purpose and benefits: The output of the AI system is data that captures a delay and the associated cause.  This will be combined with other data sources to provide a more accurate representation of a flight.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2023-02-10