GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Tableau Creator (Desktop and Prep)
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Enable analysts to connect, prepare, and visualize data.
12_outputs: visual data dashboards, reports, and predictive models. Data Analysis
Predictive Analytics
Data Classification
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Grammarly 14
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Improve writing by correcting grammar, punctuation, and style errors while suggesting tone adjustments. NLP
Grammar Correction
Tone Detection
12_outputs: Grammatically correct, well-structured, and contextually appropriate text.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Adobe Creative Cloud with Acrobat Pro Latest Version
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Intended for enhancing creative workflows by automating repetitive tasks, improving accuracy of search functions, and enabling more advanced image manipulation and video editing. Object recognition
Image Generation
NLP
12_outputs: Images, Videos, Content
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Microsoft Project Office 365
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Scheduling meetings or appointments or setting reminders using AI.
11_purpose_benefits: Assists project managers in planning, scheduling, and managing resources. Project Scheduling
Task automation
Resource Allocation
12_outputs: Optimized project timelines, resource schedules, and predictive alerts.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Stata/MP 18
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Provides advanced statistical tools for data scientists and researchers to analyze large datasets and build predictive models. Statistical modeling
Data Analysis
Predictive Analytics
12_outputs: Statistical analysis reports, regression models, and predative insights based on large data sets.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Dragon Legal Individual (Speech Recognition) 
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Helps legal professionals transcribe spoken language into text.
12_outputs: Outputs transcriptions of legal dictations and spoken languages.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Speechify Text to Speech Voice Reader Chrome Extension 9
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Converts written text into natural-sounding speech.
12_outputs: Synthesized speech from written text in various voices and languages.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Adobe Creative Cloud / Adobe Express AI feature
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: Helps users create visual content quickly through AI-driven automation.
12_outputs: Graphics, posters, and social media content. Image Generation
Content Creation
Graphic Design Automation
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Westlaw Precision & CoCounsel
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Law & Justice
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Helps professionals find relevant case law, statutes, and legal precedents.
12_outputs: Legal documents, case analysis, and predictions for legal outcomes.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Kurzweil
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Kurzweil 1000 makes printed or electronic text accessible to people with blindness and visual impairments, speaking text aloud in a variety of natural-sounding voices that can be modified to individual preferences.
12_outputs: NLP: text to voice. Audio output from text input
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Microsoft Office Suite
3_agency: Department of Labor
3_abr: DOL
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Microsoft Office Suite is a collection of productivity software applications developed by Microsoft. It is widely used for various personal, educational, and business tasks
12_outputs: create slideshows combining text, images, and multimedia. creating, editing, and formatting text documents
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: GovChat
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Mission Support
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this application is to provide a generative AI chatbot tool to EPA staff. It is materialized as a web site user interface which users interact with on their web browser when on the EPA network.
12_outputs: The Use Case produces a variety of output depending on what users use it for internally which can range from content, code, and other uses of ChatGPT. A rules of behavior requires that users do not use it for final decisions or recommendations. Additionally, users will potentially use this environment for a broad set of general AI uses. Individual use cases may be described if the system becomes widely adopted and patterns of use are determined.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2024
19_date_acq_dev_began: 3/1/2024
20_date_implemented: 12/10/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: No data is used to train, etc the model.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/usepa/msft-chatbot-ui/
40_has_ato: Yes
41_system_name: EPA Azure Cloud Hosting System 
42_dev_tools_wait: 6-12 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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Transitioning historical help desk knowledge to new help desk software 
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Air and Radiation
8_topic_area: Energy & the Environment
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: The program has 20 years of help requests and responses in a customized open source help desk tool. We are mapping out a transition to Jira Service Desk but are not able to easily import 20 years of knowledge and answers to common questions. The project team plans to use generative AI to categorize questions and summarize responses. These outputs would serve as the knowledge base for the new help desk. 
12_outputs: Knowledge base to populate the new help desk system:
- Common questions
- Categories of questions
- Responses
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Emission monitoring program knowledge management system search and summary
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Air and Radiation
8_topic_area: Energy & the Environment
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Provide a chatbot to respond to EPA monitoring system analyst questions about the emission monitoring programs, including historical regulatory changes and the reasons for those changes. 
12_outputs: Using preambles and regulatory text, respond to questions such as:
- What is the grace period for performing a relative accuracy quality assurance test?
- In what year were the monitoring performance criteria last updated and what was the justification?
- What monitoring options are available for an oil-fired power plant?
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Identification of lead service lines in drinking water distribution systems using bioindicators/biomarkers
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Reseach and Development
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Explore the potential of predictive tools to identify lead service lines (LSLs). Alternative approach using bioindicators/biomarkers obtained from high-resolution microbiome profiles to identify LSLs without excavation.
12_outputs: Predict LSLs in DWDSs . Two outcomes (binary): LSL or non-LSL.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2024
2_use_case_name: Generative AI for Study Evaluation and Document Chat
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Research and Development
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Reduce the time required to complete study evaluation and make it easier to find/recall information found in documents.
12_outputs: The outputs are generated text based upon a prompt. The text is intended to help the user answer questions about a document, specifically about the quality of the information reported.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/1/2024
2_use_case_name: Natural Gas STAR Videos 
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Air and Radiation
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This is a commercial product that is not reflected in the list in the previous column. The purpose of using this AI tool was to simply create realistic audio narration files, without having to go through the lengthy and more expensive process of hiring a voice actor to read our scripts. The tool turns text into speech.


12_outputs: The AI tool did not make any of the decisions or predictions.  It only sounded out text that we created and supplied the tool.

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/17/2023
2_use_case_name: Agency Records Management System (ARMS)
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Mission Support
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: purpose: to improve accuracy of Records schedule prediction
expected benefit: reduce administrative burden on EPA staff 
12_outputs: the machine learning model predicts records schedules 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2020
19_date_acq_dev_began: 10/1/2020
20_date_implemented: 11/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: we used a sub-set of agency records
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.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Agency Records Management System (ARMS Application)
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: No
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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Use of Microsoft CoPilot within the M365 Purview Security Suite
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Mission Support
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Purview is one of the Agency’s tools to protect our Microsoft cloud data and endpoint devices.  Several potential use cases are possible with the addition of CoPilot into the product which is anticipated in upcoming releases within our GCC tenant.  Our proposed use of AI tools is limited in scope the Agency’s existing security boundary, data and Authority to Operate (ATO).

The expected benefits are faster answers to common questions about the nature of EPA's security and IT infrastructure environment.  We will be able to more effectively leverage the voluminous amount of data currently collected by Purview, Microsoft Security Center, and Defender agents to make decision in a more expedited fashion.  We will be able to act on results more quickly to help reduce security vulnerabilities and weaknesses.


12_outputs: a.	Security and infrastructure analysts will be able to use CoPilot to better understand and report on existing security vulnerabilities on Microsoft Windows devices. Purview currently delivers canned reports in our dashboard which shows existing vulnerabilities.  CoPilot can be queried to ask specific questions about the vulnerabilities, such as which computers are impacted and the potential mitigation strategy for specific vulnerabilities.
b.	CoPilot can be used to analyze and report on installed software, reporting on potential vulnerabilities and versions for each package.
c.	CoPilot can describe and summarize Data Loss Prevention (DLP) alerts that require Agency attention and help analysts to understand which types of sensitive data are triggering alerts and to improve our models.
d.	CoPilot can quickly respond to common questions about the environment without writing custom reports:  how many Windows 22H2 computers are in the environment?  How many systems are missing the latest Microsoft System Center Configuration Manager (SCCM) client?  How many computers are still unpatched for the Log4J vulnerability?  What is the last login for the workstation name: R07Smith123?
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/1/2024
2_use_case_name: Use of AI tools within the Agency’s instance of ServiceNow
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Mission Support
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: ServiceNow is used as the Agency’s primary service desk management system for resolving incidents, problems, change controls, knowledge management, inventory of systems and software including the central Configuration Management Datbase (CMDB).  Employees and IT professionals rely on this platform to obtain service, submit incidents, obtain reports and trends, and plan workloads and priorities to solve problems.  Our proposed use of AI tools is limited in scope the Agency’s existing security boundary, data and ATO.  The expected benefits are reduced numbers of service tickets and calls which will have a direct cost reduction.  Help desk and IT professionals throughout the agency will be able to leverage the vast trove of knowledge articles, asset inventory and history of hundreds of thousands of service tickets to find answers to challenging (and common) IT issues facing our workforce.
12_outputs: Several use cases have emerged to leverage ServiceNow's upcoming AI tools for employees to engage with an AI agent to ask technical questions to solve specific technical issues.  

For example:
b.	An employee may ask, how do I reset my password; the answer will provide a written response with steps based on our existing internal knowledge articles and a link to the Agency’s self-service password management page at pss.epa.gov.  This could divert thousands of calls away from service desk agents each year.
c.	A service desk agent may ask, how do I troubleshoot a problem a customer reports with the error message at Windows login, “domain not found.”  The response would be a list of steps both the technician and employee will need to preform to resolve the error.  This will save the technician a significant amount of time to search the ServiceNow knowledge articles, read the contents and relay instructions to the customer.
d.	A manager may ask the AI agent, how many computers in Region 10 require updates to the latest version of Windows 11 23H2?  How many installations of Adobe Acrobat (pro) are installed in HQ?  How many Dell Latitude 5340 computer have expired warranties agency-wide, provide a detailed list including service tags? 

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
2_use_case_name: Machine learning-assisted literature screening
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Reseach and Development
8_topic_area: Science & Space
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The purpose is to help scientists in screening references by using machine learning to rank references by title and abstract based on relevance as determined from a set of "on topic" and "off topic" references. The benefits are that scientists will more efficiently identify relevant references for consideration in the development of scientific assessments. 
12_outputs: The machine learning tools support scientists in screening references by providing those references in a ranked order. That ranked order may or may not be based on seed references that are "on topic" and/or "off topic. The screening tools build a model based on what the screener identifies for "inclusion" and "exclusion". This ranked set of references allows scientists to more quickly identify relevant references. Some of these tools also provide an estimate or recall, or the percentage of relevant references identified. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Data Management and Analytics Platform (DMAP) Operations and Governance AI Use Cases
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Mission Support
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Improve data quality, operational efficiency, and improved use of data to support mission outcomes
12_outputs: Proposed use of AI and ML to enhance DMAP mission needs and operational excellence in the following areas:

Automated Data Quality Assessment
•	AI-augmented data profiling
•	Auto-detect data anomalies, missing values, outliers
•	Generate comprehensive data quality reports
•	Recommend data cleaning and transformation strategies
•	Provide statistical insights about dataset characteristics
AI-Augmented Data Documentation Generation
•	AI to aid in creating human-readable data dictionaries
•	Generate insights into semantic relationships between data elements
•	AI-augmented data lineage and provenance tracking documentation
AI-Driven Data Enrichment Recommendations
•	Suggest potential external data sources for enrichment
•	Recommend feature engineering techniques
•	Identify potential data joins and correlations
•	Flag opportunities for data augmentation
Workflow and Pipeline Automation
•	AI to aid in Extract Transform Load/Extract Load Transform (ETL/ELT) pipeline configurations
•	Create reproducible data transformation scripts
•	Develop dynamic data routing and processing workflows
•	Implement intelligent error handling and recovery mechanisms
Intelligent Onboarding Assistant
•	Guide users through data and project onboarding processes and workflows
•	Provide contextual recommendations during onboarding process
•	Validate/audit project requirements and compliance needs
•	Offer interactive tutorials and best practices
Predictive Data Governance
•	Recommend data access controls
•	Detect potential compliance risks
•	Augment analysis/validation/auditing and recommendations for data sensitivity levels
•	Suggest data anonymization and masking strategies
Feature Engineering for ML and Analysis
•	Auto-generate initial machine learning features
•	Identify potential model training datasets
•	Recommend preprocessing techniques
•	Highlight dataset suitability for specific ML tasks
Semantic Data Discovery
•	Create semantic knowledge graphs
•	Enable natural language querying of datasets
•	Aid in identifying and recommending data relationships across disparate sources
•	Support data catalogue generation

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024