GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: DGMH AI Chatbot
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The DGMH AI chatbot would assist with developing an initial draft response to  inquiries where the response could have been found on our website. This would allow DGMH staff to have a starting point on a response to edit and clear. Additionally, the response could be adjusted for plain language to assist in ensuring the inquirer understands the response being sent back to them. The AI chatbot would allow inquiries to be responded to quicker and would allow staff to focus on other high priority items as well.   

The project will rely on using Retrieval-Augmentation Generation (RAG) to identify relevant information from CDC’s relevant website pages to generate relevant responses using AI. Evaluation of the project will assess how accurate the response was, how complete it was, and how much revision was needed. Evaluation would also explore the answer and if the answer that was generated was consistent throughout time if multiple people asked similar questions.   The goal is consistency of content rather than tone/plain language. The chatbot will help to reduce the turnaround time for responding to inquiries and free up time for staff to address other priorities.
12_outputs: CDC's Division of Global Migration Health receives inquires for information which is available publicly in some form but may be hard to find and requires staff responding to these inquiries to have a broad knowledge and can be time intensive to respond to. The AI Chatbot will draft an initial response to questions using content available on CDC’s public-facing webpages. Each response goes through the existing CDC clearance process regardless of if AI is used, but the Chatbot helps provide an initial draft based on previously published content. The goal is to help with responding to different inquiries the division receives, including but not limited to CDC-INFO and media. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Distiller SR: AI to screen research articles for Community Guide reviews
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This tool will be used to screen research articles that will be used to inform Community Preventive Services Task Force recommendations (a body that produces guidelines independent of CDC). 

Use of this technology may increase the speed with which systematic reviews can be conducted. This may expedite the time it takes to evaluate the effectiveness of public health programs for a CPSTF recommendation (not a CDC guideline). 
12_outputs: Machine learning will be used to more efficiently screen research articles relevant to evaluating the effectiveness of an intervention. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Evaluating Generative AI for polio containment.
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The AI will impact the data retrieval and automation process for HIV researchers, specifically in providing information from datasets and potentially generating code for desired variable names and analysis or generating information about a particular dataset.

As polio eradication progresses, containment of poliovirus becomes critical. This project aims to identify and evaluate Generative AI tools to extract information from publications in an effort to identify poliovirus in places previously thought to be polio-free.  Identification of such a tool may diversify Polio and Picornavirus Branch's surveillance portfolio and increase Branch's capability of processing large volume of data for containment purposes. This project may support CDC PPLB’s polio surveillance activity and containment efforts.
12_outputs: This AI assistant will use retrieval augmented generation (RAG) to return information related to HIV based on a user's query. Our aim is to input all dataset documentation and metadata into an index with the goal of creating a virtual assistant. This assistant offers support to our HIV researchers by providing the information they seek from the data and potentially generating SAS, R, or Python code for desired variable names. For instance, if we require a dataset containing HIV PrEP information, we can request it to generate a list of current datasets that contain data on PrEP medication usage in 2019, along with the associated variable names. Subsequently, we can instruct it to produce lines of code, such as a logistic regression, which can be readily copied and pasted into our analysis software of choice. Another example would be if our researchers would like information returned about a given dataset, this assistant could return specific information based on the researcher's query.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Respiratory Virus Response (RVR) Data Analysis Concept
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This solution will impact the process of summarizing subject matter expert (SME) interpretations/knowledge during a response. 

Response efforts require prompt turnaround of essential information to the public. This project will impact public health by improving the efficiency of dissemination of information through production of key takeaways which can then be quickly reviewed by SMEs and cleared. This project will also allow for improved understanding of limitations and uncertainties in use of AI methods that could contribute to improved implementation of such methods in public health.
12_outputs: The goal of this solution is to evaluate how AI can be implemented in the process of summarizing subject matter expert (SME) interpretations/knowledge to improve efficiency in knowledge dissemination during a response.  As part of the proof-of-concept, we  seek to evaluate the limitations and uncertainties of AI methods in this use-case, such as bias and hallucinations as such issues will require mitigation before this method can be broadly adopted. Our proposed approach would make use of the ChatGPT interface through EDAV (or alternative accepted platform) as our prompts will include uncleared data from the Respiratory Virus Response (RVR). In this response,  SMEs from different groups provide bulleted information that then needs to be further summarized to address key takeaways.   Bullets from prior weeks will prompt AI to generate summaries. The initial approach will focus on engineering prompts to get key takeaways while future iterations may require fine-tuning to allow for better contextualization of responses as well as improvements in tone and style. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: School LLM initial abstract review process 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Reviewing research related to school readiness science. CDC/DRRS' School Preparedness unit is responsible for developing research around readiness science. As part of this a large language model was used to abstract themes and code publications that were identified as related to school closure to aid us in supporting our research. If funding were to become available, we would like to develop this static, one-time product into a modifiable tool that could be used for ongoing article abstraction across school and other settings and population.

The AI allows us to efficiently categorize thousands of abstracts in a much shorter time-frame with much less human time required into a user-friendly dashboard. 
12_outputs: The AI used a LLM to extract data from an abstract review and categorize relevant themes and topics into a user-friendly dashboard. This dashboard can be used by a health scientist to pull resources from 2012-2022   for specific school closure outcomes or themes.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: ChatCDC - CDC Enterprise Generative AI Chatbot (Data Extraction)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose tool to assist with extracting information from larger pieces of text. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. 

From larger documents which may be time consuming to find specific pieces of information, this AI system enables staff to extract key pieces of information to be used in later tasks. This can include action items from meeting transcripts, specific discussion focuses from larger meetings, specific mentions, or other such information. this makes the finding of information from larger document far faster and reduces the manual burden of reading larger documents for one or two specific pieces of information.
12_outputs: This chatbot, powered by Azure OpenAI Large Language Models, will generate responses to questions asked by the user, in this case about some larger piece of text and any specific pieces of information to extract. The chatbot's output can then be copied and pasted or manually entered into the appropriate testing process by the staff to ensure any code used from the chatbot's output meets the criteria of the current task.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 11/1/2023
20_date_implemented: 2/1/2024
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Yes. A series of pilot projects was conducted to evaluate the performance of this chatbot and these pilot projects are included within this inventory.
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
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: 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: ChatCDC - CDC Enterprise Generative AI Chatbot (Ideation)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose to assist with the development of ideas or to overcome writers block in different tasks. When doing a task, staff can ask this tool to ask questions to help evolve any ideas being developed. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. 

This tool enables CDC staff to have the productivity benefits of Generative AI powered chatbots without any of the security risks of 3rd party AI powered general purpose chatbots. This ensures staff can provide information regarding a current task along with general information with current tasks to help organize thinking, actions to take, and developing ideas for staff to use or reject as a part of their process. This reduces the time spent by staff dealing with situations such as writers block.
12_outputs: This chatbot, powered by Azure OpenAI Large Language Models, will generate responses to questions asked by the user. The chatbot's output can then be copied and pasted or manually entered into the appropriate testing process by the staff to ensure any code used from the chatbot's output meets the criteria of the current task. The output must be copied and pasted or manually entered somewhere for the output to be used by staff.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 11/1/2023
20_date_implemented: 2/1/2024
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Yes. A series of pilot projects was conducted to evaluate the performance of this chatbot and these pilot projects are included within this inventory.
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
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: 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: ChatCDC - CDC Enterprise Generative AI Chatbot (Summarization)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose tool to summarize and synthesize information from larger documents such as meeting transcripts, research articles, or other documents. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. 

This tool enables CDC staff to have the productivity benefits of Generative AI powered chatbots without any of the security risks of data uploads to third party tools. Using ChatCDC, Staff can have smaller, more easily digestible summaries to save staff time from reading larger documents if the time to read the full document is not available.
12_outputs: This chatbot, powered by Azure OpenAI Large Language Models, will generate responses to questions asked by the user, in this case a summary of a larger document. The chatbot's output can then be copied and pasted or manually entered into the appropriate testing process by the staff to ensure any code used from the chatbot's output meets the criteria of the current task.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 11/1/2023
20_date_implemented: 2/1/2024
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Yes. A series of pilot projects was conducted to evaluate the performance of this chatbot and these pilot projects are included within this inventory.
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
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: 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: DFWED Food Vehicle Investigation Module
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Foodborne outbreak investigation

Outbreak investigation by providing estimates of output of potential food vehicles can increase speed of tracking foodborne outbreaks in terms of identification of hypothesis generation. This reduces operational burdens in foodborne response efforts.
12_outputs: NLP and Food Vehicle Investigation Module (FVIM) uses language learning models and Text Classification Tools to identify potential vehicles. NLP tool uses text classification to provide probabilities that a food is associated with the outbreak based on reports in the SEDRIC line list. FVIM identifies past outbreak with similar key demographic and temporal characteristics to the selected outbreak to guide hypothesis generation. FVIM analyses reported food exposures uploaded to the SEDRIC line list and categorizes responses into food commodity types that are then compared to FoodNet Population survey results weighted by age, sex and ethnicity and generates a statistical comparison
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2022
19_date_acq_dev_began: 7/1/2022
20_date_implemented: 5/1/2023
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: Data are used in outbreak/response scenarios, such as foodborne illness outbreak response. Data used is dependent on the situation and outbreak, and may be owned by CDC, FDA, USDA, State Health Departments, Tribal Health Departments, Local Health Departments, Territorial Health Departments, or other entities.
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.
39_code_link:  
40_has_ato: Yes
41_system_name:  1 CDC Data Platform (1CDP)
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.