GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Detecting Overlapping Science (DOS)
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 detects applications that represent potential duplicate funding (funding the same research in different projects). It examines applications as they are submitted to the NIH and sends a report to relevant personnel in the agency.

Detect and prevent duplicate funding with real-time examination of incoming grant applications.
12_outputs: Input: Text of grant applications.
Output: Pairs of possible duplication.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 1/1/2023
20_date_implemented: 1/1/2023
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: IMPACII
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: 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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Detection of Implementation Science focus within incoming grant applications
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: Grants management oversight and administration.

Assign the grant application to a particular division for routine grants management oversight and administration.
12_outputs: This tool uses natural language processing and machine learning to calculate an Implementation Science score that is used to predict if a newly submitted grant application proposes to use science that can be categorized as "Implementation Science."
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2019
20_date_implemented: 1/1/2020
22_dev_method: Developed with contracting resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Leverages NIH application data from IRDB.
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: Yes
41_system_name: Dimensions for NIH and Analyst Support Infrastructure (ATO Letter - https://nih.sharepoint.com/:b:/s/NHLBI-ITAC-SECURITY/Eb_TarkpZPRCnX5RsGEBgQ8BgK4XC2_hjlsAVeHHLEjdww)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: No
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: Division of International Services Customer Service Chatbot
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: The initial version is designed to allow both foreign nationals and administrative officers (AOs) to interactively receive accurate answers to general questions about immigration policy and operations managed by NIH Division of International Services.

The AI data store is compiled from DIS Frequently Asked questions and other relevant policy and general information. The benefit of the AI is to alleviate manual calls to the division for questions that can be covered more efficiently and accurately.
12_outputs: Generates text, images and data. Input data source is a local database. Frequency is TBD.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024
2_use_case_name: Enhance NLM Website Accessibility
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: NLM is pilot testing the use of artificial intelligence (AI) to enhance the accessibility of the NLM website in alignment with Section 508 standards.  This initiative is critical to mitigate the enterprise risk associated with non-compliance with Section 508 standards covering web sites. The AI will focus solely on identifying and resolving accessibility errors associated with Section 508 standards for websites, including suggestions from the U.S. Web Design System framework when possible. The AI is currently being developed behind NIH's firewall for a select internal user base. The tool will not make direct changes to general web design or web content. The AI will integrate with existing web content management systems and accessibility compliance tools (like pa11y).  The AI will use website data (HTML, CSS, JavaScript) and accessibility compliance standards as input and will be deployed in the web development and IT departments, particularly those focused on accessibility and compliance.

The project may reduce costs and time associated with manual accessibility evaluation and remediation. Additionally, it is expected to enhance the accuracy and quality of accessibility features on the website. Importantly, the project aims to improve the user experience by making the site more accessible to a broader audience, thus increasing user access and discovery. Ultimately, this initiative will help reduce the enterprise risk associated with non-compliance and ensure that NLM's website meets the evolving standards for government web accessibility.
12_outputs: Inputs: Website data (HTML, CSS, JavaScript), accessibility standards.

Outputs: Accessibility issue reports, suggested code fixes, task lists for implementation.

Frequency: The AI performs both site-wide scans and single URL audits on demand. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 5/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 75N97023A00004/75N97023F00005
31_data_catalog: Yes
33_agency_data: Website data (HTML, CSS, JavaScript), accessibility standards. Measures taken to ensure data accuracy and reliability: grounding, testing, evaluation, NIST framework.
34_data_docs: Documentation is complete
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 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: Enhancing Responses to Customer Questions about NLM Products
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: NLM is piloting the creation of an application which uses the GPT family of models from OpenAI (via Azure) and data retrieved from public facing NIH/NLM web domains to answer questions about select NLM products. This is currently being developed behind NIH's firewall for a select internal user base. The artificial intelligence (AI) will generate responses to questions but will not engage in direct conversations with customers. Additional safeguards would make sense in a production environment. The AI will integrate with a knowledge base and use text data from user technical questions and knowledge base articles. It will be deployed within customer service and technical support offices.

Primary Objectives: Provide accurate and timely responses to technical questions about NLM products.

Anticipated Positive Outcomes: Improved efficiency and accuracy in customer service, better user experience, and higher satisfaction rates.
12_outputs: Inputs: User technical questions, knowledge base articles.

Outputs: AI-generated responses, gold standard responses, knowledge base links.

Frequency: Responses generated in real-time based on user inquiries.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 5/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 75N97023A00004/75N97023F00008
31_data_catalog: Yes
33_agency_data: Website data (HTML, CSS, JavaScript), accessibility standards. Measures taken to ensure data accuracy and reliability: grounding, testing, evaluation, NIST framework.
34_data_docs: Documentation is complete
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 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: Environmental health research annotation for model development
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: By creating annotated datasets that account for diverse study types, the project will enable the development of AI and machine-learning models that can significantly reduce the time and effort required to identify and capture critical data from environmental health studies. This improvement will streamline tasks for teams conducting literature assessments and reviews, improving their efficiency.

The primary objective of this AI use case is to enhance the development of datasets specifically focused on toxicology and environmental health. The key problem it aims to address is the current scarcity of relevant training datasets or corpora, which limits the ability to create effective AI models for identifying and extracting environmental health data from published studies. This challenge is compounded by the diverse nature of environmental health research, which includes various study designs such as epidemiological studies, in vitro research, exposure assessments, and experimental animal studies.  
12_outputs: Input: published environmental health studies.

Output: annotated corpus to support model development (e.g., BRAT format).
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2020
19_date_acq_dev_began: 1/1/2020
22_dev_method: Developed with contracting resources.
31_data_catalog: No
34_data_docs: Documentation is missing or not available
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Federal IT Acquisition Reform Act (FITARA) Tool
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: NIAID administers contracts to execute mission initiatives.

The tool's classifications inform SOW clarifications/revisions relevant to the FITARA.
12_outputs: The tool classifies contract statement of work (SOW) documents as being IT-related or not. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2017
19_date_acq_dev_began: 1/1/2017
20_date_implemented: 7/1/2017
22_dev_method: Developed with contracting resources.
23_contract_piids: Not Reported
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Yes, a dataset was curated using NIAID SOWs which were manually labelled to train the classification model.
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: 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: Generative AI (GenAI) Still Image Tagging
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: The artificial intelligence (AI) use case will focus on identifying still images within the NLM Digital Collections that lack descriptions. This use case will employ Generative AI (GenAI) tools such as Amazon Web Services (AWS) Rekognition and/or AWS Bedrock to identify concepts and embedded text within these images. The AI system will not generate metadata for images that already have detailed descriptions or analyze images outside the NLM Digital Collections. The AI will connect with existing NLM metadata management systems, where the generated metadata will be stored and indexed. The primary types of data used will include image files from the digital collections and textual data derived from embedded text within those images. The AI system will be deployed within the NLM's business area, focusing specifically on enhancing the search and retrieval capabilities of the NLM Digital Collections.

The use case aims to automatically generate accurate metadata for under-described images in the NLM Digital Collections, enhancing their searchability and discoverability. This improvement will increase access to valuable medical and scientific resources, supporting research and public health initiatives. 
12_outputs: Input consists of image binaries (tif, jpeg or PDF format) served from Collections website can be hosted on S3; Amount consists of untagged images within the repository, ballparked about 75,000 image objects, then about 10-100 per month based on monthly content ingestions.

Output should be AI generated classifications tags and/or AI generated image summary in text form. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
2_use_case_name: Grant Application Subject-Matter Classification Tool
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 algorithm is used to recommend a specific scientific area within the Division's portfolio with a specific confidence level. This reduces the time it takes to assign a Program Class Code and personnel assignment to every grant application received by NIEHS. It searches for key words within the title, abstract and specific aims in each grant application to determine the best scientific category. It is not used in combination with any other data sets. It uses grant application data from IMPAC II, which is imported into the Grants Funding Decision Tool (a NIEHS Custom Product).

Natural language processing of grant applications to assign scientific topics and corresponding Program Officer staff.  The subject-matter classification tool improves the efficiency of the grant administration process by automatically assigning incoming applications a subject matter classification based on the text content of their abstracts using a model derived from past human-classified examples. This classification helps route each application to the program officer with the greatest expertise in the inferred field of study.
12_outputs: Training set consists of an Excel spreadsheet containing human-classified grant proposal abstracts. Input file is an Excel table containing new unclassified grant proposal abstracts. Output is input file updated to include tool-assigned subject matter classifications.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2016
19_date_acq_dev_began: 1/1/2016
20_date_implemented: 2/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids: Not Reported
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Input data is provided to NIEHS by grant applicants, via the 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: No
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: HIV-related grant classifier tool
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 front-end application for scientific staff to input grant information, which then runs an automated algorithm to classify HIV-related grants. 

The tool allows OAR staff to determine the best categorization for a grant application after reviewing data and visualization of confidence levels through a heat map. Natural language processing of grant applications to assign scientific topics and corresponding Program Officer staff.  The subject-matter classification tool improves the efficiency of the grant administration process by automatically assigning incoming applications a subject matter classification based on the text content of their abstracts using a model derived from past human-classified examples. This classification helps route each application to the program officer with the greatest expertise in the inferred field of study.
12_outputs: Input: Grant text data.

Output: Predicted categorization.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/1/2022
19_date_acq_dev_began: 12/1/2022
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: Grant text data (Title, Abstract and Specific Aims).
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: 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: Improving Customer Response
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: NLM is pilot testing the utilization of artificial intelligence (AI) to assist Customer Service Representatives (CSRs) in drafting responses to customer inquiries. It is currently being developed behind NIH's firewall for a select internal user base. The AI is focused on generating draft responses for customer service but will not make final decisions or engage in direct communication with customers. CSRs are instructed to not enter personally identified info into the AI system. In the event this info is entered, the AI system is automatically prompted to not include any personally identified information in its generated response. Additional safeguards would make sense in a production environment or if it was released to the public. The AI will integrate with customer service platforms and databases like Medline Plus and DailyMed. The AI will use text data from customer inquiries and publicly accessible NLM-managed biomedical information services and be deployed within the customer service department. 

The project aims to enhance customer response metrics, increase customer satisfaction, and improve the efficiency of handling customer service inquiries.
12_outputs: Inputs: Customer inquiries, publicly accessible, NLM-managed biomedical information services (such as Medline Plus, DailyMed, etc.).

Outputs: Draft responses, resource links, disclaimers.

Frequency: Responses generated in real-time as inquiries are received.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 5/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 75N97023A00004/75N97023F00006
31_data_catalog: Yes
33_agency_data: Website data (HTML, CSS, JavaScript), accessibility standards. Measures taken to ensure data accuracy and reliability: grounding, testing, evaluation, NIST framework.
34_data_docs: Documentation is complete
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 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.