GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Tool for PO Lookup Assignment (TPAL)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Similar to publicly reported IRM use case. The Tool for PO Assignment Lookup (TPAL) provides NIGMS Program staff with the ability to automatically search for appropriate Program Officers (POs), including their corresponding Program Area Codes (PACs), and NIH ICs to assign or reassign project proposals, or to provide advice to prospective applicants. TPAL uses natural language processing and machine learning to predict the three most relevant NIGMS POs, including their corresponding PACs, and the three most relevant NIH ICs from the project description/abstract text entered into a textbox. Note that because the PO and IC prediction algorithms are independent of each other, TPAL will always try to provide the most relevant program​ officers, even when NIGMS is not one of the top three recommended NIH ICs.  

There are many occasions in which Division Directors, Branch Chiefs, and Program Officers wish to receive suggestions for the most appropriate people to talk to about a project proposal or where to send a proposal that might not be appropriate for NIGMS.
12_outputs: Input: Free form text in an online textbox. 

Output: Top three most relevant ICs and POs and their probabilities. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2020
19_date_acq_dev_began: 3/1/2020
20_date_implemented: 7/1/2020
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: All data comes from the internal NIH IMPAC II database.
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: No
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: 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: Transformative Research Award Anonymization Check (TRAAC)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This tool helps to determine whether grant applications for NIH Director's Transformative Research Awards are properly anonymized by quickly detecting information that might identify the applicant in the Specific Aims or Research Strategy sections. 

This tool automatically screens many pages and highlights any text that might allow identification of the applicants. The highlighted text is then examined by subject matter experts. This streamlines content screening and aids both NIH staff and the external scientific community in making more rapid and efficient determinations about whether anonymity has been compromised. 
12_outputs: Input: PDF files including text and images from two sections of the NIH grant applications.

Output: Spreadsheets containing records of identified text/phrases as well as PDF files with text/phrases highlighted. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
20_date_implemented: 1/1/2022
22_dev_method: Developed in-house.
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: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: IMPACII 
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: Center for Scientific Review  General Support System (CSR GSS) 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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: Machine learning pipeline for mining citations from full-text scientific articles
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The NIH Office of Portfolio Analysis developed a machine learning pipeline to identify scientific articles that are freely available on the internet  and do not require an institutional library subscription to access. The pipeline harvests full-text pdfs, converts them to xml, and uses a Long Short-Term Memory (LSTM) recurrent neural network model that discriminates between reference text and other text in the scientific article. The LSTM-identified references are then passed through our Citation Resolution Service. For more information see the publication describing this pipeline: Hutchins et al 2019 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000385#sec003).

Retired use case
12_outputs: The pipeline harvests full-text pdfs, converts them to xml, and uses a Long Short-Term Memory (LSTM) recurrent neural network model that discriminates between reference text and other text in the scientific article. The LSTM-identified references are then passed through our Citation Resolution Service.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/1/2023
2_use_case_name: Machine learning system to predict translational progress in biomedical research
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: A machine learning system that detects whether a research paper is likely to be cited by a future clinical trial or guideline. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community’s early reaction to a paper. For more information see the publication describing this system: Hutchins et al 2019 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000416)

Retired use case
12_outputs: A machine learning system that detects whether a research paper is likely to be cited by a future clinical trial or guideline. 
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/1/2023
2_use_case_name: Assessing developing possible version of "HHSGPT" for OIDP-specific internal use
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: OASH
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: OIDP's HIV.gov: an exploration of possibly using a custom knowledge-based walled/isolated version of "Chat GPT" for our internal OIDP users to access information to help with internal operational efficiencies related to our work on national strategies.  We have been meeting for almost 1 year with senior HHS officials working on AI to keep them up to date on our approach and efforts in both RAG LLM and generative AI. We describe our work as an internal library. The tool is called Ask ABE! (v3.0.0-alpha.1) - AI-Driven. Beneficial. Equitable (ABE). It's like having a digital librarian who can help us at OIDP easily access and use all the valuable resources from HIV.gov to support our work in ending new HIV transmissions! Our work is critical to ensuring the following issues are addressed: (1) Security: FedRAMP certified Azure Open AI 3.5 Turbo / 4o Global. (2) Privacy: Not tracking conversations nor usage. PII stripped. (3) Equity: HIV Language and HIV Language Guideline through internal system prompt. (4) Hallucinations: Internal system prompt  - ABE HIV.gov knowledge base and conversation history. There is a low risk of this occurring compared to open systems like ChatGPT. (5) Risk: NIST AI Risk Management Framework: Govern, Map, Measure, Manage for Responsible AI, Observable AI, and Explainable AI (6) Certifications / Training / Support: Azure AI Certifications / Data Science DoJo / Corporate Thought Leadership Support: Responsible AI Framework.
12_outputs: OIDP internal federal staff would possibly be able to ask questions, develop content, and revise existing content using a unique dataset of OIDP information. Inclusion of NIH HIV guideline is embedded in the internal prompting. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/1/2023
2_use_case_name: Informal generative AI research for OIDP's HIV.gov
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: OASH
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: OIDP's HIV.gov monitors (1) new and emerging AI tools/policies, generative AI use in the field of communications, and (2) the impact of generative AI on HIV federal content delivery through major search engines. This work is done by an ORISE fellow in conjunction with the HIV.gov communications team. The information is synthesized and discussed during a weekly communications meeting to determine any possible application or if additional follow-up is needed. The purpose of this activity is to determine potential tools that we may be able to use to extend the reach of our work. For example, we have been monitoring tools that potentially can synthesize weekly content into a short narrated video.
12_outputs: This practice helps us guide our planning.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/1/2022
19_date_acq_dev_began: 11/1/2022
22_dev_method: Developed with both contracting and in-house resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation has been partially completed
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: Presenting/providing technical assistance on HIV.gov's cautious & transparent use of generative AI
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: OASH
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: OIDP's HIV.gov hosts generative AI workshops, 1-on-1 trainings, and limited generative AI labs to share our cautious and transparent use of generative AI to create communications content. These activities also include information on generative AI basics and information on federal guidance. Our limited AI-generated content is disclosed with a watermark or a tag saying, "Created by AI, Reviewed By Humans." These trainings have occurred at 5 national HIV-related conferences and include multiple 1-1 training sessions and workshops that reinforce the importance of following best practices, being transparent, and being cautious. We have trained over 500 people on the cautious and transparent use of generative AI.
12_outputs: These offerings help our audiences understand the importance of cautious and transparent use, federal resources, and additionally promote information about our office's HIV programs.
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: 3/1/2023
22_dev_method: Developed with both contracting and in-house resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
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: 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: Utilizing HHSGPT Pilot for writing/editing 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: OASH
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Limited OIDP staff have been selected to have access to HHSGPT, an internal generative AI tool in its pilot phase. Team members have been using it for editing, brainstorming, idea generating, drafting text, revisions, gap analysis, organization/structure, evaluations, etc. HHSGPT has been used to assess national strategies, such as the 2026-2030 vaccine national strategy, the 2026-2030 viral hepatitis strategy, and others. When utilizing HHSGPT, we work to report back how this use has reduced labor burden and note any interesting findings when using the tool. Use reporting is done on an Excel file. 
12_outputs: HHSGPT uses a closed tool to generate suggestions, responses, edits, and information that may be useful for OIDP tasks related to infectious diseases or HIV information/policies. Team members have been using it for editing, brainstorming, idea generating, drafting text, revisions, gap analysis, organization/structure, evaluations, etc.  
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
20_date_implemented: 10/1/2023
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: HHS
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: Not Reported
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: 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: HHSGPT
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: OCIO
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: ChatGPT is an AI-powered chatbot developed by OpenAI, and it can be integrated with Azure, Microsoft's cloud computing platform. ChatGPT leverages the power of OpenAI's language model to provide natural language understanding and generation capabilities.

Scalability: As an AI-powered chatbot, ChatGPT can handle multiple conversations simultaneously, scaling up to meet high demand without the need for additional human resources.
Increased Efficiency: ChatGPT can automate repetitive tasks, freeing up human agents to focus on more complex or value-added activities, improving overall efficiency.

Persona Benefits:
Assist data scientists in data exploration, model prototyping, analysis, collaboration, and accessing relevant knowledge, enhancing their productivity and efficiency.

Assist policy analysts by providing instant insights, answering complex policy-related questions, and facilitating interactive discussions, enabling faster and more informed decision-making. 
Assist in writing large policy documents by providing suggestions, generating coherent content, and offering language refinement, streamlining the writing process and ensuring high-quality outputs.
12_outputs: At present, HHSGPT is undergoing a pilot phase throughout the entire HHS organization under an IATT.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/1/2023
19_date_acq_dev_began: 9/1/2023
22_dev_method: Developed in-house.
31_data_catalog: No
34_data_docs: Documentation has been partially completed
40_has_ato: Yes
41_system_name: HHSGPT
42_dev_tools_wait: Less than 6 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: 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: DECIDE
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: SAMHSA
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: At present, SAMHSA's Office of Behavioral Health Equity (OBHE) manually reviews grantee organizations' progress towards achieving their health disparity reduction goals. This process involves manually extracting Disparity Impact Statements, annual reports, enrollment data, and external population data to verify the accuracy of grantees' reported progress. This is a time-consuming process, thus the development of an AI-based tool for doing this review is in development.

DECIDE will reduce the administrative burden on OBHE staff and allow them to provide feedback to grantees more quickly.
12_outputs: DECIDE will use generative AI to automate the process of extracting data from various sources, then comparing across data sources to determine (a) whether a grantee has accurately reported the demographic data of the population they serve and (b) how much progress they have made towards decreasing health disparities for the vulnerable population they have identified.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024
2_use_case_name: Document drafting and editing
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: SAMHSA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: SAMHSA employees routinely write internal reports, public-facing written products, and other documents. Some parts of the writing process, such as structuring thoughts into an outline and proofreading, can be enhanced through the use of AI.

The document drafting and editing tool will increase employee productivity and help produce polished written work products.
12_outputs: The document drafting and editing tool will serve as a writing assistant to help SAMHSA employees present their ideas clearly and effectively. It will function similarly to a standard large language model chatbot, allowing the user to query the model to perform specific writing tasks, such as outlining and proofreading.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: Document summarization
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: SAMHSA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: SAMHSA employees routinely read and summarize large amounts of documentation for grantmaking, management, and other day-to-day work. Ingesting and condensing such vast amounts of information can take time away from SAMHSA's most important mission-driven work.

The document summarization tool will expedite day-to-day workflows and help employees ingest large amounts of information more quickly.
12_outputs: The document summarization tool will allow users to upload documents and produce a summary of these documents. The tool will have enhanced data privacy mechanisms. Additionally, the tool will not use any SAMHSA-provided input as training data, ensuring that private information stays private to SAMHSA.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: Smart Search
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: SAMHSA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: SAMHSA.gov team would like to procure "Kendra" to implement a smart search on store.samhsa.gov—improving access to the Federal Government’s leading source of behavioral health information. Currently the search on store.samhsa.gov relies on key words pulled from document titles, descriptions, and manually generated tags. Both anecdotal evidence and results of usability testing have demonstrated that the Store’s search capability is the single greatest barrier to users accessing materials they need

Kendra will allow for scanning of all documents in the store to respond more accurately to consumer queries while providing a faster search. 
12_outputs: Kendra is not generative AI and does not provide AI-generated responses to queries. Kendra operates within strict guardrails to ensure the security and privacy of information, particularly concerning the handling of sensitive data like Personally Identifiable Information (PII).
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/1/2024