GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Scanner Data Product Classification
3_agency: Department of Labor
3_abr: DOL
4_bureau: BLS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 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 saves
12_outputs: Entry Level Item codes
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2018
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 3/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: NO
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Agency Generated; respondent-provided category and product description information
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: BLS Internal system
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Yes
2_use_case_name: Expenditure Classification Autocoder
3_agency: Department of Labor
3_abr: DOL
4_bureau: BLS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Purpose: assign a reported expense description from Consumer Expenditure Diary Survey respondents to expense classification categories known as item codes. Benefits: improve productivity, cost savings
12_outputs: BLS-internal item codes (expenditure classification categories)
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2019
19_date_acq_dev_began: 1/1/2022
20_date_implemented: 1/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1605TA-21-F-00064
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Agency-internal; expenditure descriptions and corresponding item code assignments
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: BLS Internal system
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Yes
2_use_case_name: Generative AI Assistant
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 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.
12_outputs: 

The AI system provides recommendations.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/15/2023
19_date_acq_dev_began: 8/15/2023
20_date_implemented: 8/16/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1605DC19A0004
NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Agency datasets are not necessary for the development of this use case.
33_agency_data: No specific dataset. The AI engine came ready off the shelf, and the final product has been tested ad hoc by agency users and their NLP free text data. 
34_data_docs: Documentation is widely available
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Azure Open AI and AWS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Yes
2_use_case_name: Occupation Code Suggestion For Job Duties Data
3_agency: Department of Labor
3_abr: DOL
4_bureau: ETA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Suggest appropriate occupation code for job duties data using natural language processing  and classification techniques
12_outputs: Automated Occupation code suggestions
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 8/16/2024
2_use_case_name: Notice of Deficiency (NOD) Text Generation using Generative AI model
3_agency: Department of Labor
3_abr: DOL
4_bureau: ETA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Application of custom Generative AI model to create Notice of Deficiency (NOD) text based on the input data
12_outputs: Natural language text generation
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/17/2023
2_use_case_name: PII Redaction
3_agency: Department of Labor
3_abr: DOL
4_bureau: OSHA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Using Amazon Web Services Personal Identifying Information scrubber for ITA text fields and Named Entity Recognition to remove additional names from the text fields.
12_outputs: Input text returned with personal identifying information removed.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 5/20/2024
19_date_acq_dev_began: 5/20/2024
20_date_implemented: 11/19/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids:  NNG15SD22B
24_hisp_support: NO
26_public_service: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: YES
31_data_catalog: Yes
33_agency_data: Agency Generated
34_data_docs: Documentation is available
35_demo_features: Yes
37_custom_code: No
38_code_access: YES
39_code_link: Yes
40_has_ato: Yes
41_system_name: AWS
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: YES
53_real_world_testing: Yes
54_key_risks: Risk of PII been published
55_independent_eval: Yes – by the CAIO
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_question_type: Other – Immediate human intervention is not practicable; however, an appropriate fail-safe has been implemented.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: NO
62_disparity_mitigation: Review
63_stakeholder_consult: General solicitations of comments from the public
2_use_case_name: Website Chatbot Assistant
3_agency: Department of Labor
3_abr: DOL
4_bureau: ODEP
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The chatbot helps the end user with basic information about the Workforce Recruitment Program and information on who to contact.
12_outputs: Generate content from the WRP program
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/18/2024
19_date_acq_dev_began: 3/18/2024
20_date_implemented: 8/16/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: NNG15SD22B
 1605DC19A0004
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: YES
29_contains_pii: No
30_saop_review: NO
31_data_catalog: NO
33_agency_data: Agency provided list of questions and answers.
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes
39_code_link: https://aws.amazon.com/lex/
40_has_ato: Yes
41_system_name: AWS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Yes
2_use_case_name: Initial Determinations for incoming applications
3_agency: Department of Labor
3_abr: DOL
4_bureau: ETA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: AI tool to help perform initial analysis of the incoming 9141 and 9089 applications. Make initial determinations regarding whether issuance of a Request for Information is necessary. Create a standard RFI request and either (1) automatically issue the RFI before case assignment and hopefully obtain customer responses by the time the case is assigned OR (2) perform this action at case assignment and allow the assignment analyst to affirm and issue the RFI or not.
12_outputs: Flag cases for RFI 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/21/2024
2_use_case_name: AI Course Design Assistant
3_agency: Department of Labor
3_abr: DOL
4_bureau: OSHA
8_topic_area: Education & Workforce
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: AI tool to recommend the structure of a course, titles for modules, descriptions, and images. AI-powered algorithms analyze course content and quickly generate test questions and prompts for authentic assessments. (Blackboard/COTS Product, unmodified)
12_outputs: Generated text and images for Course outlines, descriptions, tests/assessments.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Worker PLUS Microsimulation Program
3_agency: Department of Labor
3_abr: DOL
4_bureau: CEO 
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: 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)
12_outputs: The model simulates specific leave-taking behavior and outcomes (including number of leaves, leave lengths, benefit levels, and benefit eligibility) with individual workers in a state using data from the five-year American Community Survey (ACS) Public Use Microdata Sample (PUMS)
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/27/2017
19_date_acq_dev_began: 9/27/2017
20_date_implemented: 3/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: DOLQ129633247
24_hisp_support: NO
27_public_info: Yes
28_iqa_compliance: All CEO products adhere to the principles of rigor, relevance, transparency, independence, and ethics.
29_contains_pii: No
30_saop_review: NO
31_data_catalog: Other
31_question_type: All data is public, and not necessarily owned by DOL. 
33_agency_data: CEO/WHD 2018 FMLA Employee Survey, American Community Survey
34_data_docs: Documentation is widely available
35_demo_features:  Gender/Sex, Age, Income, Employment status
37_custom_code: Yes
38_code_access: Yes
39_code_link: https://www.dol.gov/resource-library/microsimulation-model-worker-leave
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Yes
2_use_case_name: Computer-Assisted Coding: SOII Autocoder
3_agency: Department of Labor
3_abr: DOL
4_bureau: BLS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 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. The use of SOII autocoder initially began in 2012 for review purposes, then gradually expanded to automatically assign these classification codes. In reference year (RY) 2022, 92% of all SOII elements were automatically coded, which were then subsequently validated by human staff.

The SOII Autocoder is a transformer-based text classification model using millions of SOII cases as the labeled training data and a publicly available, third-party language model as a pre-trained base model. The Autocoder is trained annually in house using a GPU server that is owned and maintained by OCWC. Once trained, the Autocoder is deployed internally via REST API, autocoding batches of SOII and MSHA cases on a routine basis during the production cycle, all within the BLS network.
12_outputs: SOC and OIICS codes
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/12/2012
19_date_acq_dev_began: 1/13/2013
20_date_implemented: 1/15/2015
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: NO
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Agency Generated
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: BLS Internal System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Yes
2_use_case_name: CFOI Record Matching
3_agency: Department of Labor
3_abr: DOL
4_bureau: BLS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 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.

The CFOI Record Matching programs use various aspects of AI in the areas of natural language processing and record matching. The CFOI-OIS matching uses random forest classifier model trained on previously matched CFOI-OIS data. The CFOI-QCEW matching uses TF-IDF vectors and cosine similarity to match establishment names in the CFOI-QCEW data. And the CFOI-CPDMS matching uses a publicly available, third-party question-answering language model to identify relevant CFOI elements from CPDMS data and then uses random forest classifier model to match CPDMS data to CFOI data. These processes are all conducted within the BLS network, and the outputs, in the form of spreadsheets, are provided once or twice a year to the staff in the CFOI program for review. These processes are all run within the BLS network.
12_outputs: Similarity score between records
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/18/2024
19_date_acq_dev_began: 10/18/2024
20_date_implemented: 9/20/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: NO
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Agency Generated
34_data_docs: Documentation is complete
35_demo_features: Age and Sex/Gender
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: BLS Internal System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Yes
2_use_case_name: OIICS Coding
3_agency: Department of Labor
3_abr: DOL
4_bureau: OSHA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 

Auto-code assigning Occupational Injury and Illness Classification (OIICS)
12_outputs: 

Manual coding would be a time-consuming task, estimated to require around 65,500 labor hours for initial coding, plus additional time for error correction.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/4/2024
2_use_case_name: AI Assisted coding -Microsoft GitHub Copilot
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Automatically generate software code using Microsoft Copilot, this will reduce the time for generating code
12_outputs: Automatically generate software code using Microsoft Copilot, this will reduce the time for generating code
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2024
19_date_acq_dev_began: 9/1/2024
20_date_implemented: 9/1/2024
22_dev_method: Developed in-house.
23_contract_piids: NNG15SD60B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Azure
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: CPS OTC Prediction
3_agency: Department of Labor
3_abr: DOL
4_bureau: BLS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: 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. The benefit of this AI use case is increased accuracy.
12_outputs: 

worker's time off was paid, or whether they get paid hourly or not
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 2/1/2022
20_date_implemented: 11/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: NO
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Agency Generated
34_data_docs: Documentation is complete
35_demo_features: Class of worker, education, marital status, family income, sex, age
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: BLS Internal system
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Yes