GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Generative AI for Form 1040X: Tax Examiner Assistant
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Develop a workflow assistant that leverages generative AI to consolidate and analyze complex policies from various documents (e.g., IRMs, SERP, desk guides). This tool will leverage Large Language Model (LLM) to provide real-time, accurate guidance for Tax Examiners handling individual amended returns. Future iterations will incorporate additional features that may include generating correspondence for taxpayers that prioritizes their actions. Key stakeholders, including Research, Applied Analytics, and Statistics (RAAS), Information Technology (IT), Submission Processing, and Accounts Management, will collaborate to enhance existing RAAS capabilities, integrate new documents, and fine-tune the solution for this specific use case. The tool will be made available to a select pool of Tax Examiners, who will verify and validate the outputs of the tool prior to finalizing the processing of the amended returns. Benefits and impacts include operational efficiency, reduce processing times, and provide timely, accurate guidance.
12_outputs: The 1040X Tax Examiner Assistant tool produces a comprehensive response with a plain text summary of the answer with relevant resources and links to the resources. The tool also outputs a feedback prompt where Tax Examiners can provide user feedback.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/3/2024
19_date_acq_dev_began: 9/12/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 2032H5-24-P-00086
24_hisp_support: No
31_data_catalog: No
33_agency_data: Text and excerpts from publicly available, non-sensitive data from Internal Revenue Manual (IRM) 21, IRM 3, and Document 6209.
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.
40_has_ato: No
42_dev_tools_wait: Less than 6 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: 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: ServiceNow Generative AI Pilot
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: As part of a Chief Technology Officer (CTO) sponsored objective to enable and utilize Generative Artificial Intelligence (Gen AI) capabilities the User and Network Services Information Technology (IT) Service Desk in cooperation with Enterprise Operations will be implementing a limited production challenge to support AI generated Incident Summarization; providing agents with concise summary of case notes and history, Resolution Notes generation; generating accurate resolution notes based on actions taken and solution achieved, and Knowledge Article Generation; generating complete knowledgebase articles base on incident or case records. With the above we expect to see significant time savings when conducting warm handoffs between teams, while reducing the mean time to restore incidents. Additionally, the effort should ensure high-quality resolution notes are generated to assist in solving future incidents, while feeding existing knowledge review and publication processes potentially empowering self-service capabilities which will reduce time to closure activities.
12_outputs: Incident Summarization; providing agents with concise summary of case notes and history, Resolution Notes generation; generating accurate resolution notes based on actions taken and solution achieved, and Knowledge Article Generation; generating complete knowledgebase articles base on incident or case records
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Circular Dependencies (Individual Master File Modernization)
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Purpose/Objective of the AI Project: To accelerate resolution of circular dependencies (cases where calls to classes create dependencies upon themselves Class A --> Class B --> Class C --> Class A) using Large Language Model (LLM)
How It Accomplishes Its Purpose: Classes involved in circular dependencies are provided to the LLM and resolutions are proposed for the end user to accept
Decision or Judgment Automated by the Model: Resolution of circular dependencies identified in 	Individual Tax Processing Engine (ITPE) codebase
Human Review of Artificial Intelligence (AI) Output: Yes, human review is required before accepting any changes
Anticipated Benefits: Proposed resolutions for circular dependencies which are projected to accelerate the process from months to hours
Direct or Ultimate Impact: Accelerating resolution of circular dependencies in ITPE
Population Impacted by This Decision: ITPE Architects and Developers
12_outputs: Recommendations on resolutions of circular dependencies identified in ITPE.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/11/2024
2_use_case_name: Synthetic Test Data Generation (Individual Master File Modernization)
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Purpose/Objective of the Artificial Intelligence (AI) Project: The primary objective of this AI project is to create rich and voluminous synthetic data for testing purposes. This synthetic data will be instrumental in enhancing the quality and comprehensiveness of various testing processes.
How It Accomplishes Its Purpose: At a high level, the project achieves its goal by ingesting data model schemas and generating scripts. These scripts are then used to create synthetic test data, ensuring that the generated data closely mimics real-world scenarios and datasets.
Decision or Judgment Automated by the Model: This AI tool automates the creation of synthetic data for testing purposes. It systematically generates data that can be used in various testing scenarios without the need for manual data creation.
Human Review of AI Output: Yes, humans are involved in reviewing the AI-generated synthetic data prior to taking action on the results. This step ensures that the data meets the required standards and is suitable for the intended testing purposes.
Anticipated Benefits: The project is expected to provide a robust framework for generating synthetic test data. This framework will facilitate the creation of comprehensive and high-volume test data, which is essential for conducting thorough performance, regression, and other types of tests.
Direct or Ultimate Impact: The ultimate impact of this decision is the availability of more comprehensive and increased volumes of test data. This enhanced data will support various testing activities, leading to more reliable and effective testing outcomes.
Population Impacted by This Decision: The primary population impacted by this decision includes testers and end users across ITPE and Individual Master File (IMF). These stakeholders will benefit from improved testing processes and outcomes.
12_outputs: Synthetic Test Data customized to user specification / requirements.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/21/2024
2_use_case_name: Auto Tech Debt Remedy (Individual Master File Modernization)
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Purpose/Objective: The purpose of this Artificial Intelligence (AI) project is to utilize industry-standard scanning tools and generative AI capabilities to automate the resolution of technical debt in source code.
How It Accomplishes Its Purpose: The project identifies technical debt using industry-standard scanning tools such as SonarQube. The output from these tools is then fed into a large language model, which generates the modified, resolved code. This code is integrated into an automated pipeline to streamline the entire process.
Decision or Judgment Automated: The model automates the writing of code to resolve and remediate technical debt.
Human Review Process: Yes, humans are involved in reviewing the AI output prior to taking action. Specifically, a pull request (PR) is created in GitHub through this tool, and no code is pushed without human review.
Anticipated Benefits: The primary benefit anticipated from this project is the reduction of technical debt.
Direct or Ultimate Impact: The direct impact is reduced technical debt, leading to more maintainable and efficient codebases.
Population Impacted: The immediate population impacted by this decision includes software developers. In the long run, end users will also benefit as the software will be maintained more easily and efficiently.
12_outputs: Large Language Model (LLM) recommendations for code remediations identified from application scanning tools.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/21/2024
2_use_case_name: M365 Copilot
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: An established group of participants across multiple business areas will test Artificial Intelligence-based features and functions available within the existing M365 applications using M365 Copilot.  The testing results will validate the security, benefits and capabilities that support the existing architectural boundaries of M365.


12_outputs: User defined prompts
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Copilot for Power Platform
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: An established small group of participants across multiple business areas will test Artificial Intelligence-based features and functions available within the existing Power Platform applications using Copilot.  The testing results will validate the security, benefits and capabilities that support the existing architectural boundaries of Power Platform.

12_outputs: N/A. This is an Artificial Intelligence assistant that will support development activities.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/13/2024
2_use_case_name: Copilot for M365 (Criminal Investigation Unit)
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: 1 **Word**: Copilot can help draft, edit, and summarize documents, providing suggestions for grammar, style, and tone improvements.
2. **Excel**: It can analyze data, generate charts, and create complex formulas, making data management more efficient.
3. **PowerPoint**: Copilot assists in designing presentations, suggesting slide layouts, and generating content for slides.
4. **SharePoint**: It enhances collaboration by organizing and managing documents, automating workflows, and providing insights into document usage.
5. **Outlook**: Copilot helps manage emails by drafting responses, summarizing long email threads, and organizing your inbox.
6. **Teams**: It facilitates meeting preparation by generating agendas, taking notes, and providing meeting recaps.
12_outputs: 1 **Word**: Copilot can help draft, edit, and summarize documents, providing suggestions for grammar, style, and tone improvements.
2. **Excel**: It can analyze data, generate charts, and create complex formulas, making data management more efficient.
3. **PowerPoint**: Copilot assists in designing presentations, suggesting slide layouts, and generating content for slides.
4. **SharePoint**: It enhances collaboration by organizing and managing documents, automating workflows, and providing insights into document usage.
5. **Outlook**: Copilot helps manage emails by drafting responses, summarizing long email threads, and organizing your inbox.
6. **Teams**: It facilitates meeting preparation by generating agendas, taking notes, and providing meeting recaps.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Customer Experience Analytics
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Extract insights from data sources in the customer service domain to better understand service interactions and create new opportunities to improve the customer experience.
12_outputs: Summary of call transcripts between IRS and Taxpayer
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/2/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 2032H8-21-F-00179
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Call Transcripts
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.
40_has_ato: Yes
41_system_name: RAS-1
42_dev_tools_wait: Less than 6 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: 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: Exercise Design
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: DF-OCCIP
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Expedite the exercise development process through more rapid event design and scenario creation as well as streamline post-event reporting. Access to a Large Language Model (LLM) can significantly enhance an exercise program primarily in two ways. First, by improving the development of scenarios and second, post-event analysis. For scenario development, an LLM can quickly generate realistic and complex scenarios tailored to specific objectives and characteristics. For example, in a cybersecurity exercise, the LLM can create detailed narratives involving various types of cyber incidents, such as ransomware attacks, data breaches, and phishing schemes. It can simulate the actions of adversaries, the impact on IT infrastructure, and the necessary response measures. This variety ensures participants are exposed to a wide range of potential cyber threats, enhancing their preparedness and response capabilities. 
12_outputs: Detailed event descriptions, scenario information, and/or reporting and analysis.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/8/2024
2_use_case_name: Incident Coordination and Reporting Support
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: DF-OCCIP
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Reduce development time for products by rapidly summarizing latest information and generating a draft product for human review. Access to a LLM could significantly reduce the time needed for Incident Coordinators to develop (and edit) the first version of incident related products. Inputs from public and potentially non-public sources could quickly be ingested by a model and refined into the products based on desired templates and characterizations. Additionally, if the model uses live data, it could be used for research and gathering updates as the incident progresses. The benefits of having access to a LLM are particularly significant at the outset of an incident when all members of the incident response function are trying to manage multiple inputs from a variety of internal and external sources while attempting to generate products for socialization with key partners. 
12_outputs: Quick turn reports based on existing templates for sharing with partners 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/8/2024
2_use_case_name: EFTPS Fraud Monitoring
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Bureau of the Fiscal Service
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The Electronic Federal Tax Payment System (EFTPS) Fraud Initiative performs fraud detection within the Direct Pay and EFTPS Online payment channels. It supports near-real-time identification, profiling, scoring, and alerting of suspicious transactions and behavior. When user activity is captured, the model compares features of the activity/payment to EFTPS historical data and produces a score based on assessed risk level. This risk score generates an alert in the solution case manager for further review by a fraud analyst. The results of the [human] review could warrant blocking of the user account, monitoring of future activity, or escalation to law enforcement for further investigation.
12_outputs: Risk Score for Potential Fraud Prevention
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Digitization of Microfilm
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Bureau of the Fiscal Service
8_topic_area: Mission-Enabling
10_commercial_ai: Digitizing text from scanned documents or smart forms for archival purposes using AI.
11_purpose_benefits: In support of the microfilm conversion process for Mature Undredeemed Debt/SavingsBonds, the Fiscal Vendor, Iron Mountain, has incorporated a focus on the application of AI and deep learning ML to maximize processing efficiency and accuracy of results.  The solution leverages these technologies to continually optimize output and ultimately our return on investment.  Iron Mountain's solution uses AI/ML to make data more accessible and enabled Fiscal Service to establish the abilit to search the data and identify bond owners.  To date, 175,000 reels of film have been digitized for approximately 2.1B images that are ultimately stored in Azure cloud tenant.
12_outputs: Image Files
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Check Fraud Pipeline
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Bureau of the Fiscal Service
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Since the dramatic spike in Economic Impact Payments and Advance Child Tax Credit checks, there has been a sharp uptick in U.S. Treasury check fraud.  Specifically, fraudsters have been stealing U.S. Treasury checks and altering the names on the checks. The situation is not typically detected until a payee claimed non-receipt to issuing agency (often long after fraud occurred, also after time limits to recover from FI).  Additionally, Law Enforcement has been challenged in recovering evidence (e.g., ATM footage) to prosecute, as well as identifying organized fraud at the transactional level.  This pilot has enabled the streaming of check images into the payment analytics environment and is leveraging AI/ML to read check image and check settlement data for comparison against check issue data.  To date, 30 million check images and settlement transactions have been analyzed identifying 12K fraudulent altered items worth over $100M
12_outputs: Risk Score for Potential Fraudulent Checks
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Gender differentiated credit scoring
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Inclusive Growth, Partnerships, and Innovation
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: University of California, Berkeley, is building a machine learning model to conduct gender differentiated credit scoring for customers of Rappicard in Mexico. They will compare this ML model to Rappi's "status quo" model to determine whether a gender differentiated model leads to greater access to credit for women.
12_outputs: Credit scores
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 7/14/1905
19_date_acq_dev_began: 7/14/1905
20_date_implemented: 7/15/1905
21_date_retired: 7/15/1905
2_use_case_name: Using ML for predicting treatment interruption among PLHIV in Nigeria
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Global Health (GH)
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Using data from USAID funded Strengthening Integrated Delivery of HIV/AIDS Services (SIDHAS) project in Nigeria we trained and tested an algorithm that can be used for predicting the probability that someone newly initiated on ART will interrupt treatment. The algorithm has been successfully integrated into the Lafiya Management Information System (LAMIS), the individual-level client level electronic medical record system. Each week the outputs, for each new patient is shared with staff at the health facilities and those at high risk are provided with more intensive follow up support to reduce the risk of treatment interruption. We also conducted a qualitative assessment among to health care workers at the facilities to determine their perception of ML and determine what additional support are required for institutionalizing ML into their routine work.   
12_outputs: Patient risk scores
16_dev_stage: Retired
17_impact_type: Both
18_date_initiated: 7/13/1905
19_date_acq_dev_began: 7/13/1905
20_date_implemented: 7/13/1905
21_date_retired: 7/15/1905