One of the key benefits to managing and utilizing data more efficiently is that it enables the use of advanced capabilities such as artificial intelligence (AI) and machine learning (ML). The Department of Labor has recently begun exploration of how these advanced technologies can be used to benefit the agency and help deliver on our mission. In an effort to create transparency in the adoption of these tools, this page serves to highlight the various uses of AI across the department.
Active AI Use Cases
ID | AI Use Case Name | Summary of Use Case | Stage of Development | AI Technique |
---|---|---|---|---|
ID 1 | AI Use Case Name Form Recognizer for Benefits Forms | Summary of Use Case Custom machine learning model to extract data from complex forms to tag data entries to field headers. The input is a document or scanned image of the form and the output is a JSON response with key/value pairs extracted by running the form against the custom trained model. | Stage of Development Operation and Maintenance | AI Technique Classification machine learning model involving computer vision |
ID 2 | AI Use Case Name Language Translation | Summary of Use Case Language translation of published documents and website using natural language processing models. | Stage of Development Implementation | AI Technique Cloud based commercial-off-the-shelf pre-trained NLP models |
ID 3 | AI Use Case Name Audio Transcription | Summary of Use Case Transcription of speech to text for records keeping using natural language processing models. | Stage of Development Operation and Maintenance | AI Technique Cloud based commercial-off-the-shelf pre-trained NLP models |
ID 4 | AI Use Case Name Text to Speech Conversion | Summary of Use Case Text to speech (Neural) for more realistic human sounding applications using natural language processing models. | Stage of Development Operation and Maintenance | AI Technique Cloud based commercial-off-the-shelf pre-trained NLP models |
ID 5 | AI Use Case Name Claims Document Processing | Summary of Use Case To identify if physicianÕs note contains causal language by training custom natural language processing models. | Stage of Development Implementation | AI Technique Natural language processing for (a) document classification and (b) sentence-level causal passage detection |
ID 6 | AI Use Case Name Website Chatbot Assistant | Summary of Use Case The chatbot helps the end user with basic information about the program, information on who to contact, or seeking petition case status. | Stage of Development Implementation | AI Technique Cloud based commercial-off-the-shelf pre-trained chatbot |
ID 7 | AI Use Case Name Data Ingestion of Payroll Forms | Summary of Use Case Custom machine learning model to extract data from complex forms to tag data entries to field headers. The input is a document or scanned image of the form and the output is a JSON response with key/value pairs extracted by running the form against the custom trained model. | Stage of Development Initiation | AI Technique Classification machine learning model involving computer vision |
ID 8 | AI Use Case Name Hololens | Summary of Use Case Provides a realistic virtual hazardous workspace that affords students the opportunity to recognize hazards without physical exposure to hazards. | Stage of Development Operation and Maintenance | AI Technique |
ID 9 | AI Use Case Name DOL Intranet Website Chatbot Assistant | Summary of Use Case Conversational chatbot on DOL intranet websites to help answer common procurement questions, as well as specific contract questions. | Stage of Development Initiation | AI Technique Cloud based commercial-off-the-shelf pre-trained NLP models |
ID 10 | AI Use Case Name Official Document Validation | Summary of Use Case AI detection of mismatched addresses and garbled text in official letters sent to benefits recipients. | Stage of Development Implementation | AI Technique Computer Vision |
ID 11 | AI Use Case Name Electronic Records Management | Summary of Use Case 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. | Stage of Development Initiation | AI Technique Custom text classification machine learning model |
ID 12 | AI Use Case Name Call Recording Analysis | Summary of Use Case Automatic analysis of recorded calls made to Benefits Advisors in the DOL Interactive Voice Repsonse (IVR) center. | Stage of Development Initiation | AI Technique Cloud based commercial-off-the-shelf pre-trained NLP models |
ID 13 | AI Use Case Name Automatic Document Processing | Summary of Use Case Automatic processing of continuation of benefits form to extract pre-defined selection boxes. | Stage of Development Implementation | AI Technique Cloud based commercial-off-the-shelf pre-trained NLP models |
ID 14 | AI Use Case Name Automatic Data Processing Workflow with Form Recognizer | Summary of Use Case Automatic processing of current complex worflow to extract required data. | Stage of Development Initiation | AI Technique Classification machine learning model involving computer vision |
ID 15 | AI Use Case Name Case Recording summarization | Summary of Use Case Using an open source large language model to summarize publicly available case recording documents which are void of personal identifiable information (PII) or any other sensitive information. This is not hosted in the DOL technical environment and is reviewed by human note takers. | Stage of Development Development and Acquisition | AI Technique Large language summarization model |
ID 16 | AI Use Case Name OEWS Occupation Autocoder | Summary of Use Case 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. | Stage of Development Operation and Maintenance | AI Technique Natural Langauge Processing, Logistic Regression, Classification |
ID 17 | AI Use Case Name Scanner Data Product Classification | Summary of Use Case 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. | Stage of Development Operation and Maintenance | AI Technique Natural Langauge Processing, Logistic Regression, Classification |
ID 18 | AI Use Case Name Expenditure Classification Autocoder | Summary of Use Case Custom machine learning model to assign a reported expense description from Consumer Expenditure Diary Survey respondents to expense classification categories known as item codes. | Stage of Development Development and Acquisition | AI Technique Natural Language Processing, Random Forest, Classification |
Contact Us
U.S. Department of Labor
200 Constitution Ave NW
Suite N-1301
Washington, DC 20210
Chief AI Officer: Mangala Kuppa
Email: zzOCIO-AI@dol.gov