GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Unaccompanied Children Process Model Digital Twins
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of Refugee Resettlement (ORR) Unaccompanied Children Bureau provides a safe and appropriate environment to children who enter the United States without immigration status and without a parent or legal guardian who is able to provide for their physical and mental well-being (referred to as “unaccompanied children”). ORR funds residential care provider facilities that provide temporary housing and other services to unaccompanied children in ORR custody. ORR and its care providers work to ensure that children are released timely and safely from ORR custody to parents, other family members, or other adults (often referred to as “sponsors”) who can care for the child’s physical and mental well-being. Unaccompanied children remain in ORR’s care and custody until they are released to a parent or other sponsor in the United States, are repatriated to their home country, obtain legal status, or turn 18 years old, at which time they are transferred to the custody of DHS. There are many processes involved throughout.

The goal is for ORR to be more proactive in strategic planning and contingency planning.
12_outputs: ORR is exploring use of process digital twins to provide the ability to run "what if" scenarios
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
2_use_case_name: Analyzing Public Comments on Proposed Rule
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: In the federal rulemaking process, the public has an opportunity to provide comments on proposed new regulation. AI was used to support the public comment review process for a proposed rule to make quality improvements in the Head Start program.

The public comment analysis pipeline provided an Excel spreadsheet of comment segments that were tagged and Word files of topic-based summary drafts. Subject matter experts from the Office of Head Start reviewed comments by topic and further analyzed and refined the topic-based summaries.

The benefit of using AI for this purpose is improved baseline information following public comment to support more expeditious final rule development.
12_outputs: A large language model (LLM) was used to tag public comments by: topics selected primarily by policy experts; sentiment; and intent (e.g. question, suggestion) alongside first drafts of topic-based summaries. 
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 2/1/2024
2_use_case_name: Outreach List Segmentation
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of External Affairs (OEA) is responsible for sharing information from ACF with external constituents. OEA had a long list of constituent email addresses that were without context. They wanted the ability to send targeted emails to different groups of people whose organizations work in particular policy spaces.

The AI tool returned a spreadsheet of the provided domains with structured information on likely organization name, summary of their mission, policy domains (e.g. public health, education), and website. This spreadsheet allows OEA to segment their lists and target organizations more efficiently.
12_outputs: An AI-enabled search tool was used to research likely organization name, purpose, and policy space based on the domains (e.g. "@organization.com")  from contact email addresses.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 8/1/2024
2_use_case_name: Ask HR Policy
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This project provides a resource for ACF Executive Officers/Administrative Officers and managers to easily navigate Human Resources (HR) Policy documentation and quickly get answers to their questions that are based on the official HR Policy Library that is on the HHS.gov website (https://www.hhs.gov/about/agencies/asa/ohr/hr-library/index.html)

Ask HR Policy provides a secure interface permissioned only to select ACF Executive Officers and Administrative Officers.  Users type in questions about managing employees covered by the HR Policy Library, and Ask HR Policy provides a narrative of its “thought process” then suggests an answer based in the documentation, alongside links that take the user to the relevant section of the official document.
12_outputs: We are using LLMs to analyze the questions inputted by users. The AI model synthesizes the provided documentation sources and then summarizes and answers the users questions with cited sources.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 5/1/2024
20_date_implemented: 5/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75P00122A00010/75P00122F37003/P00004
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: No training or fine-tuning. HHS HR Policies were used as a data source for retrieval-augmented generation.
34_data_docs: Documentation is complete
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: ACF Discover (HHS instance of Palantir Foundry)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Child Welfare Information Gateway OneReach Application
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The Childrens Bureau runs the Children Welfare Information Gateway, a connection to trusted resources on the child welfare continuum. The Information Gateway has a hotline for answering questions or requesting information: https://www.childwelfare.gov/stay-connected/contact/

The Information Gateway hotline automates some of the more routine questions and requests, allowing staff to focus on more complex, nuanced situations.
12_outputs: The Information Gateway hotline connects to a phone interactive voice response (IVR) managed by OneReach AI. OneReach maintains a database of state hotlines for reporting child abuse and neglect that it can connect a caller to based on their inbound phone area code. Additionally, OneReach offers a limited FAQ texting service that utilizes natural language processing to answer user queries. User queries are used for reinforcement training by a human AI trainer and to develop additional FAQs.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2020
19_date_acq_dev_began: 4/1/2020
20_date_implemented: 6/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids: 20EMPO0106
27_public_info: Yes
28_iqa_compliance: This use case is operated in compliance with HHS's agencywide Information Quality Act policy and procedures.
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is widely available
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
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: 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: Collective Bargaining Compass
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This project provides a resource for ACF managers to easily navigate Collective Bargaining Agreement documentation and quickly get answers to their questions that are based on the official 400-page rulebook.

The Collective Bargaining Compass provides a secure Virtual Assistant interface permissioned only to select ACF managers.  Users type in questions about managing employees covered by the Collective Bargaining Agreement, and the Virtual Assistant provides a narrative of its “thought process” then suggests an answer based in the documentation, alongside links that take the user to the relevant section of the official document.
12_outputs: We are using LLMs to analyze the questions inputted by users, which then the AI model synthesizes the provided documentation sources and then summarizes and answers the users questions with cited sources.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 2/1/2024
20_date_implemented: 3/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75P00122A00010/75P00122F37003/P00004
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: No training or fine-tuning. HHS-The NTEU 2023 Collective Bargaining Agreement was used as a data source for retrieval-augmented generation.
34_data_docs: Documentation is complete
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: ACF Discover (HHS instance of Palantir Foundry)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Discover User Assistant
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This project provides a resource for ACF Discover Staff Management Module users (i.e. ACF Executive Officers and other ACF administrative and management staff) to easily navigate ACF Discover Staff Management platform and instructional documentation and quickly get answers to their questions about the module’s features.

The User Documentation Assistant provides a secure virtual assistant interface that is only available to ACF Discover Users.  Users are able to ask the assistant specific questions about the capabilities of ACF Discover along with how to leverage tools and applications. The Virtual Assistant is able to provide answers by referencing the User Reference guide.
12_outputs: We are using LLMs to analyze the questions inputted by users, which then the AI model synthesizes the provided documentation sources and then summarizes and answers the users' questions with cited sources.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 1/1/2024
20_date_implemented: 1/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75P00122A00010/75P00122F37003/P00004
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: No training or fine-tuning. HHS-The Discover Staff Management Module User Reference Guide was used as a data source for retrieval-augmented generation.
34_data_docs: Documentation is complete
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: ACF Discover (HHS instance of Palantir Foundry)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Policy Knowledge Base Data Migration
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of Family Assistance (OFA) provides technical assistance to grantees, including answering questions about policy regarding the Temporary Assistance for Needy Families (TANF) program. Historically, OFA has tracked its accumulated TANF knowledge across disparate locations and file types.﻿

Given the quantity of knowledge base articles involved in this project (>1,100), the AI draft article topics and categories helped speed up the creation of the knowledge base minimum viable product.
12_outputs: As part of a broader effort to establish a TANF knowledge base on a unified platform, large language models were used to: 1) generate draft knowledge base article topics based on historical tracker information; and 2) categorize articles to help organize the knowledge base.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 6/1/2024
20_date_implemented: 9/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 75ACF123F80045
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Historical trackers of questions and answers to TANF policy questions
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
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: 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: Qualitative analysis
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: ACF staff often conduct surveys and interviews, which generate qualitative data that needs to be analyzed for themes and trends.

The AI functionality assists users in identifying relevant passages in their narrative data related to specific topics.
12_outputs: We use commercial off-the-shelf software that have built-in AI functionality to suggest topic tagging of qualitative data.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2023
19_date_acq_dev_began: 3/1/2023
20_date_implemented: 9/1/2023
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 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: 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.
2_use_case_name: AHRQ Search
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: AHRQ
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Organization wide search that includes Relevancy Tailoring, Auto-generation Synonyms, Automated Suggestions,  Suggested Related Content, Auto Tagging, and "Did you mean?" to allow visitors to find specific content.

This AI use case enhances our agency's efficiency and user experience by optimizing search results, auto-completing queries, suggesting relevant searches and content tags, as well as proposing spelling corrections. 
12_outputs: This AI use case aims to optimize search results by adjusting their ranking, ensuring that the most pertinent information is displayed at the top. It also enhances search effectiveness by adding synonyms to queries behind the scenes. It further improves user experience by auto-completing queries as they are being typed, and showing related searches that might offer additional valuable insights. The system also proposes content tags automatically, leveraging machine learning to assess existing content tagging patterns. Additionally, it suggests spelling corrections and reformats search queries based on data from Google Analytics.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2019
19_date_acq_dev_began: 9/1/2019
20_date_implemented: 10/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids: 75Q80121A00001
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available
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: AHRQ AWS Enclave
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: Chatbot
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: AHRQ
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This use case introduces a conversational interface that enables users to ask questions about AHRQ content using natural language. This will serve as a modern, efficient replacement for the traditional public inquiry telephone line.

Improved user experience and reduced resources responding to public inquiries
12_outputs: An interactive interface that can respond to plain language queries in real time using natural language processing
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: 2/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 75Q80121A00001
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available
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: AHRQ AWS Enclave
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: Chatbot
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: AHRQ
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: For the Patient Safety Organization Privacy Protection Center website, this use case provides an interface that allows users to conversationally ask questions about content in natural language.

Improved user experience and reduced resources responding to public inquiries
12_outputs: An interactive interface that can respond to plain language queries in real time using natural language processing
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2021
2_use_case_name: Enhancing Diversity in Peer Review - Pilot
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: AHRQ
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Division of Scientific Review (DSR) is aiming to enhance diversity in AHRQ peer review groups by revising recruitment strategies, aiming for 20% representation from underrepresented minorities in each study section. This process, involving networking, outreach, and various resources, typically takes at least three months.

By utilizing AI to swiftly and accurately identify diverse and qualified grant reviewers, our agency will foster a more efficient process for identifying scientific reviewers and a more equitable and inclusive scientific review process. This will help us achieve our goal of providing safer, high-quality, accessible, and affordable healthcare to individuals and communities throughout the U.S., thereby improving the overall effectiveness of our agency.
12_outputs: This AI use case will involve training an artificial intelligence system to examine the background of peer reviewers - taking into account factors such as expertise, race/ethnicity, gender, and geographic area - and match these inputs to the relevant study section, subsequently referring the reviewer to the Scientific Review Officer (SRO). This flexible system will empower SROs to make the final recruitment decision or to refer the expert to another study section or Special Emphasis Panel (SEP).
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024