GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Report Analysis and Archive System (RAAS)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CWMD
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: To assist users of the Test and Evaluation document archive in locating documents and portions of document that are relevant for their needs by specifying individual queries as natural language questions, responding with detailed, natural language answers, and facilitating attainment of related or more precise information through follow-up questions.
12_outputs: Intermediate outputs include context summaries to facilitate indexing of document passages. Output to the user includes summarization of content relevant to the questions and interests indicated by the conversation with the user.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2023
19_date_acq_dev_began: 10/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70RWMD20K00000016
24_hisp_support: No
31_data_catalog: No
33_agency_data: The data set available to the model is the set of CWMD Test and Evaluation Division test reports stored in the RAAS archive.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
40_has_ato: Yes
41_system_name: Report Analysis and Archive System (RAAS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: RelativityOne
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: DHS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: RelativityOne is a document review platform used to gain efficiencies in document review in litigation, FOIA, and other arenas where large-scale document review and production is necessary. It is currently in use across several different DHS Components and Offices.
12_outputs: Review Center tailors the review process by predicting the categorization of documents based on the reviewer's inputs.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2021
19_date_acq_dev_began: 5/1/2021
20_date_implemented: 8/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: Purchased as COTS in several Components.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: N/A - The Agency does not own any of the data used to train, fine-tune, and/or evaluate performance of the model(s) used in this use case.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Multiple across DHS
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: Commercial Generative AI for Text Generation (AI Chatbot)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: DHS
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: DHS employees are permitted to use commercially available generative AI for text generation in day-to-day work. Commonly referred to as AI Chatbots, these tools are able to dynamically create written content through text prompts submitted by the user. They use Natural Language Processing (NLP) and Large Language Models (LLM) to produce natural sounding language in a wide variety of contexts and styles.

Approved applications of commercial Gen AI tools to DHS business include generating first drafts of documents that a human would subsequently review, conducting and synthesizing research on open-source information, and developing briefing materials or preparing for meetings and events.
12_outputs: Outputs may include writing, answers to question, and search results, depending on inputs from the user.

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/24/2023
19_date_acq_dev_began: 10/24/2023
20_date_implemented: 10/24/2023
38_code_access: No – agency does not have access to source code.
2_use_case_name: Commercial Generative AI for Image Generation
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: DHS
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: DHS employees are permitted to use commercially available generative AI for image generation in day-to-day work. These tools are able to dynamically create graphical content through text prompts submitted by the user. They use Natural Language Processing (NLP), in conjunction with other Machine Learning techniques such as Generative Adversarial Networks (GANs) and Diffusion Models, to produce images in a wide variety of contexts and styles.
12_outputs: Outputs may include pictures in different stylesk and types, depending on the inputs from the user.

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/24/2023
19_date_acq_dev_began: 10/24/2023
20_date_implemented: 10/24/2023
38_code_access: No – agency does not have access to source code.
2_use_case_name: Commercial Generative AI for Code Generation
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: DHS
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: DHS employees are permitted to use commercially available generative AI for code generation in day-to-day work. These tools are able to dynamically create usable code through plain-language prompts submitted by the user. They use Natural Language Processing (NLP) and Large Language Models (LLM) to produce code in many different programming languages and for a variety of tasks.
12_outputs: Outputs include useable code in different programming languages, depending on the inputs from the user.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/24/2023
19_date_acq_dev_began: 10/24/2023
20_date_implemented: 10/24/2023
38_code_access: No – agency does not have access to source code.
2_use_case_name: OCFO Code Assist GPT
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: 1) Code Assist GPT is an internal facing GenaAI tool to augment the FEMA workforce in generating and troubleshooting existing queries in established query languages.  

2) The tool provides improved queries and rapid iteration to produce desired scripts/query language saving the end user 80-90% of the time versus custom query development.  The tool also opens other computer languages, as required, to support our data analytics community."

12_outputs: Code Assist GPT is an internal facing GenaAI tool to augment the FEMA workforce in generating and troubleshooting existing queries in established query languages.  The user simply enters the language they are trying to query (i.e. SQL, Java, COBOL, etc) and the Code Assist GPT then provides a proposed query based on the elements provided by the user.  If the user experiences an error or the query is not successful, the Code Assist GPT maintains the session as long as the user is still logged in and hasn't restarted the session, the user can continue to prompt the GPT to provide enhancements to the provided query until results are as expected.  At the end of the user session, all prompts/queries are removed and no data is stored outside of the active user session.

16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70FA3124F00000027
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: N/A - The Agency does not own any of the data used to train, fine-tune, and/or evaluate performance of the model(s) used in this use case.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
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: FEMA OCFO GPT
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: Geospatial Damage Assessments
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: We will integrate these automated tools for imagery-based damage assessments into RGO processes and systems to inform FEMA geospatial analysts.  FEMA geospatial analysts will review output and develop recommendations, so FEMA can quickly detect and characterize damaged and undamaged buildings, identify the concentrations of damage, identify and detect debris, and to support rapid response and recovery activities.
12_outputs: AI models generate points or polygons over areas where damage or debris is likely. This output is a recommendation meant to trigger investigation by human analysts via traditional damage assessments. In large scale incidents, imagery collection can be comprised of hundreds of thousands of miles of land and millions of structures. AI automation allows for the prioritization of imagery exploitation where damage is likely to have occurred, reducing the time required to identify impacts and summarize damage sustained. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2022
19_date_acq_dev_began: 8/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: The PIID is specific to the disaster the use case is used for.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Geospatial Damage Assessments.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
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: Other
43_question_type: We are not developing models in house, we have contracted out our AI work
45_compute_request: Other
45_question_type: We are not developing models in house, we have contracted out our AI work
47_timely_resources: Other
47_question_type: We are not developing models in house, we have contracted out our AI work
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: Planning Assistant for Resilient Communities (PARC)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: Hazard mitigation plans are not only a foundational step that communities can take to build their resilience but can be lengthy to produce and challenging for communities that lack resources to do so. PARC will specifically support State, Local, Tribal, and Territorial (SLTT) governments’ understanding of how to craft a plan that identifies risks and mitigation strategies as well as generate draft plan elements—from publicly-available, well-researched sources — that governments could customize to meet their needs. This capability could provide planners more time to allocate toward community engagement and stakeholder collaboration, and lead to more communities having the ability to submit grant applications for funding to become more resilient and reduce disaster risks.
12_outputs: The Beta Release GenAI Plan Generator (OpenAI GPT-4o) is an LLM that  generates sections of the Hazard Mitigation Plans based on user inputs. It processes prompts and user responses to create detailed, regulatory-compliant Hazard Mitigation Plan sections.  Beta Release PARC Assistant (OpenAI GPT-4o) prodices responses through an interactive chat assistant. The Assistant helps planners by answering questions related to the Hazard Mitigation Plan process, offering explanations of policy guidelines, and guiding users through FEMA’s regulatory requirements.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/27/2023
19_date_acq_dev_began: 1/29/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 70FA4020A0000000281, 70FA3020A00000008
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: The agency-owned data included: Legal/Policy Documents (i.e., federal legal and policy references, such as nondiscrimination clauses and presidential memorandums); Regulations (i.e., regulatory texts, including sections of the Code of Federal Regulations (CFR)); and Guides and Handbooks (i.e., FEMA-issued guides that provide frameworks and instructions, such as planning handbooks and operational guides).
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
40_has_ato: No
42_dev_tools_wait: 6-12 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: Recovery and Resilience Resource (RRR) Portal
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The AI-layer (currently unfunded) would include a smart matching wizard functionallity via AI large language model (LLM) to connect SLTT partners with the resources that meet their unique needs. Increasing access and improving navigation of federal resources will benefit a wide array of stakeholders across all sectors, including private and public. The primary stakeholders are SLTTs that are looking to support recovery and/or resilience in communities. This includes 50 States, DC, and 5 US Territories, about 80,000 local governments (including special districts), 574 Federally recognized Tribal governments, and their partners. Additional stakeholders include the 25+ federal departments and agencies that are members of the Mitigation Framework Leadership Group (MitFLG), who have equity in the availability of comprehensive resources, best practices, or tools for SLTTs to recover from or increase resilience to disasters.

12_outputs: The smart matching wizard will be an AI LLM that analyzes user search behavior and matches recommended resources from across the expansive data library that will include resources from federal, state, local, nonprofit sectors.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/30/2024
2_use_case_name: Digital Processing Procedure Manual (D-PPM)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The AI addresses the inefficiency in the current manual search process, where agents spend significant time navigating static documents like PDFs (2000+ pages/70 documents) to answer survivor questions. As policies can change frequently, especially during disasters, it is crucial that agents access the most up-to-date information quickly and accurately.

Expected Benefits and Positive Outcomes:
Faster Response Times: The AI will enable agents to retrieve information quickly, reducing the overall time spent on each call.
Improved Answer Accuracy: By providing more accurate and relevant search results, the AI will help agents deliver consistent, correct responses.
Cost Savings: Reducing the time spent per call will improve operational efficiency, leading to potential cost savings.
Reduced Escalations: By empowering agents with the correct information, the tool will lower the frequency of escalations to supervisors, allowing them to focus on more complex cases.
Increased Capacity for Complex Casework: The time saved through the AI tool will allow agents to handle more complex queries, ultimately improving service quality and survivor satisfaction.
Scalability: As the AI tool is rolled out, it will provide a scalable solution that can grow with the agency’s needs during large-scale disaster responses.
12_outputs: The AI will provide search results based on the agent's query, identifying the most relevant policies, procedures, and disaster-specific information. These results will be returned in real-time, enabling agents to quickly review and apply the information to their responses. While the AI does not make final decisions, it assists agents by highlighting the most applicable information for them to review.

Search Results: The AI use case will pull information on relevant policies and procedures from the prescribed policy inventory. It may generate a summary of the search results with citations of the source documents.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/2/2024
2_use_case_name: FEMA OCFO GPT
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: FEMA OCFO GPT - B is an internal facing GenAI tool to augment the FEMA workforce in generating initial responses to questions for the record and providing a preliminary response to the Program Office to use in their formal response to the request. It provides draft responses reducing the data gathering stage and providing additional time for analysis, response, and approval.  This has reduced the analyst initial level of effort versus individual research by 80% on initial surveys."
12_outputs: FEMA OCFO GPT is an internal facing GenAI tool to augment the FEMA workforce in generating initial responses to questions for the record and providing a preliminary response to the Program Office to use in their formal response to the request.  FEMA OCFO GPT leverages public facing and internal deliberative documents (hearing testimony, answers sent to previous questions, and other relevant documents) to assist in answering questions the Agency receives.  The tool generates a draft response that is then refined/updated prior to providing a formal response.  This tool is leveraged in the data gathering stage and does not replace any current analysts work or Agency leadership review prior to submittal via the formal request for information or question from an internal stakeholder or external stakeholder.
16_dev_stage: Retired
18_date_initiated: 7/1/2023
19_date_acq_dev_began: 8/1/2023
20_date_implemented: 2/12/2024
21_date_retired: N/A -Consolidated into another use case.
2_use_case_name: Incident Management Workforce Deployment Model (depmod)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: FEMA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The model is able to extract patterns from big data, combine these patterns in a way that mirrors a real-world process, and simulate likely outcomes to yield predictions in the form of relatable, mission-centric, metrics.
12_outputs: The system primarily outputs predictions that can then (optionally) be summarized and communicated as recommendations.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2021
19_date_acq_dev_began: 8/1/2021
20_date_implemented: 2/1/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: HHSN316201200112W
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The Deployment Tracking System (DTS) is currently the primary data source. It provides a historical record of IM workforce deployments that can be validated for accuracy and used for model training and prediction.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
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: 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.