GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Enhancing ClimRR Capabilities to Better Support Electric Utility Applications and Technical Assistance
3_agency: Department of Energy
3_abr: DOE
4_bureau: GDO HQ -  Grid Deployment Office (GDO)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The AI, CALLM (Climate Action through Large Language Models), addresses the problem of communicating complex climate projections and scientific literature to a broad audience, particularly electric sector stakeholders. It simplifies this information to help these stakeholders identify climate resilience solutions.

Expected benefits include improved communication of climate science, empowering stakeholders to directly address climate change impacts, and accelerating the scalability of ClimRR (presumably an existing Argonne tool) to serve a wider range of users. This ultimately leads to more effective climate resilience planning and potentially cost savings through better informed decision-making.
12_outputs: The AI output of CALLM is information synthesized from complex climate projections and scientific literature, presented in a simplified and accessible format. This output helps users understand potential climate impacts and identify appropriate climate resilience solutions. The information is grounded in vetted data and published research to minimize inaccuracies and hallucinations common in large language models. The output could range from summaries of climate-related risks, to lists of potential adaptation strategies tailored to specific situations, depending on the user's input and the function of the system it's integrated with (like ClimRR).
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/26/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
29_contains_pii: No
31_data_catalog: No
33_agency_data: The Climate Action through Large Language Models (CALLM) tool utilizes vetted climate data and published climate resilience literature to train, fine-tune, and evaluate its performance. The specific datasets are not detailed here, but the approach emphasizes the use of established climate science information to ground the model's responses and mitigate inaccuracies.
40_has_ato: No
2_use_case_name: OpenText for Records Management (File share auto-classification)
3_agency: Department of Energy
3_abr: DOE
4_bureau: EE HQ - EE Headquarters (EE)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Reduce time required to classify legacy records accumulated over 20 years.
12_outputs: OpenText will categorize each file located on the network drives.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 1/2/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: other direct costs
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Trained using existing  internal EERE records currated by subject matter experts.
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.
35_demo_features: Other
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: EE HQ LAN (aka SCORE)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: not applicable
45_compute_request: Other
45_question_type: not applicable
47_timely_resources: Other
47_question_type: not applicable
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: Funding Finder
3_agency: Department of Energy
3_abr: DOE
4_bureau: IM-50 - Architecture Engineering Technology and Innovation (IM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: The Funding Finder will aggregate FOAs from different DOE sources and enables users to ask questions when identifying opportunities and developing proposals. 
12_outputs: Answers to questions about DOE FOAs.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/10/2024
19_date_acq_dev_began: 10/1/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 89303019AIM000005
24_hisp_support: No
31_data_catalog: No
33_agency_data: The Funding Finder tool is in development and is utilizing publicly available DOE websites and FOAs.  Example websites include "https://infrastructure-exchange.energy.gov/", "https://eere-exchange.energy.gov/", "https://arpa-e-foa.energy.gov/", "https://48c-exchange.energy.gov/", "https://oced-exchange.energy.gov/", "https://ie-exchange.energy.gov/".
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: OCIO Google Cloud 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: 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: PDF Analyzer
3_agency: Department of Energy
3_abr: DOE
4_bureau: IM-50 - Architecture Engineering Technology and Innovation (IM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: PDF Analyzer will enable teams across the DOE to upload large PDFs and ask questions and generate content related to those PDFs.  
12_outputs: PDF Analyzer will output the answers to a user's question along with the relevant sections of the PDF that the answer is based on. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/14/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 89303019AIM000005
24_hisp_support: No
31_data_catalog: No
33_agency_data: For initial evaluation, the development team used publicly available documents like the  Feasibility Study - Nyngan Scandium Project PDF (https://scandiummining.com/site/assets/files/5775/feasbility_study-nyngan_scandium_project.pdf)  to test the tool.
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: Yes
41_system_name: OCIO Google Cloud Platform
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: 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: Unleashing AI Transformer Models on FPGAs for Accelerating LHC and Particle Physics
3_agency: Department of Energy
3_abr: DOE
4_bureau: FNAL - Fermi National Accelerator (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: This effort focuses on Transformer models for representation learning on Field Programmable Gate Arrays
(FPGA), in order to seamlessly integrate AI capabilities into particle physics experiments, specifically
focusing on the CMS level-1 (L1) trigger at the High-Luminosity LHC (HL-LHC) and real-time magnet
quench detection. While conventional methods for event identification have limitations, modern AI and
machine learning techniques offer superior alternatives.
12_outputs: This AI system has two fold use cases, represnetation learning for LHC Trigger and multi-modal magnet quench detection algorithms.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2024
20_date_implemented: 10/1/2027
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: research datasets from scientific experiments
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.
35_demo_features: Other
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.
63_stakeholder_consult: None of the above
2_use_case_name: GitHub Copilot with the OpenAI Codex
3_agency: Department of Energy
3_abr: DOE
4_bureau: PNNL - Pacific Northwest National Laboratory (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: This increases their productivity, allows them to focus more on innovation and research, and accelerates the development of high-quality software solutions for scientific research.
12_outputs: This increases their productivity, allows them to focus more on innovation and research, and accelerates the development of high-quality software solutions for scientific research.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
2_use_case_name: First Alert DataMinr AI
3_agency: Department of Energy
3_abr: DOE
4_bureau: NA-IM (Enterprise) – Office of the Associate Administrator for Information Management and Chief Information Officer (HQ-Enterprise) (NNSA HQ)
8_topic_area: Emergency Management
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: We use this AI service to provide spectial or situational awareness, incidents occuring around NNSA or DOE Site.  Emergency reporting, monitoring, speeds up emergency operations reporting. The AI is being used in place of a large team that would be required for coding and development of emergency services facilitation.  This would be a cost saving software for the Federal government.
12_outputs: Data aggregation and reflection.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Yurts AI search function
3_agency: Department of Energy
3_abr: DOE
4_bureau: SLAC - SLAC National Accelerator Laboratory (SC43 OIM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Locating relevant information across multiple data sources at SLAC. The expected benefit SLAC intends to receive from the use of Yurts is improved data accessibility for the userbase at SLAC

12_outputs: Data summaries and recommendations

16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/2/2023
19_date_acq_dev_began: 2/27/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: The Yurts.ai model that's implemented at SLAC is fed SLAC-internal documentation for training and use

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.
35_demo_features: Other
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: 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: 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: LANL AI Portal
3_agency: Department of Energy
3_abr: DOE
4_bureau: LANL - Los Alamos National Lab (LAFO)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Democratized access to open-source/open-weights Large Language Models (LLMs) for general purpose office productivity, research of AI models, software development, operational streamlining, code development

12_outputs: Interactive text chat replies from user prompts, summaries of documents user submitted for Retrieval Augmented Generation (RAG),  Replies to API queries from enterprise and scientific applications

16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/6/2023
19_date_acq_dev_began: 3/21/2024
20_date_implemented: 3/21/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Not trained in-house, using open-source/open-weights models. Reliant on model provider transparency 

34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_demo_features: Other
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: Authorization to Operate Amazon Web Service Federal Risk and Authorization Management Program Infrastructure as a Service Cloud Service Provider, Amazon Web Service Information System Security Plan, SD-006-CP-002/L2
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Other
47_question_type: Security change request approval for introduction of the software tools used to build the portal took >3 months to clear local field office. Compute provisioning by the LANL Cloud Services team was fast

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.
51_extension_request: No – Agency did not request an extension for this use case.
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
61_adverse_impact: No
65_appeal_process: Yes
67_opt_out: Other
67_question_type: The AI Portal is an opt-in service if they wish to interact with a large language model, users of the portal are under no obligation to go to the website
2_use_case_name: Position Description Tool
3_agency: Department of Energy
3_abr: DOE
4_bureau: IM-50 - Architecture Engineering Technology and Innovation (IM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Currently requirements are still be collected for this use case. The end goal of this use case is to  streamline the creation of position descriptions used as a critical component for hiring federal employees. 
12_outputs: Position descriptions for federal roles.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/5/2024
2_use_case_name: ChatGPT Enterprise
3_agency: Department of Energy
3_abr: DOE
4_bureau: LANL - Los Alamos National Lab (LAFO)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: General business productivity, research of AI models

12_outputs: Interactive text chat replies from user prompts, summaries of open/public/unrestricted documents user submitted for Retrieval Augmented Generation (RAG) through CustomGPTs

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/6/2023
19_date_acq_dev_began: 4/16/2024
20_date_implemented: 4/18/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Not trained on any agency or LANL data

34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_demo_features: Other
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: Other
45_question_type: N/A (SAAS platform delivery)

47_timely_resources: Other
47_question_type: N/A (SAAS platform delivery)

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.
51_extension_request: No – Agency did not request an extension for this use case.
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
61_adverse_impact: No
65_appeal_process: Yes
67_opt_out: Other
67_question_type: ChatGPT Enterprise is an opt-in service where users need to procure their licenses in order to choose interaction with OpenAI tools
2_use_case_name: Argo
3_agency: Department of Energy
3_abr: DOE
4_bureau: ANL - Argonne National Laboratory (SC43 OIM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Enables anyone with the Argonne community to leverage text-based generative AI with their Argonne-specific information and data, including sensitive research or operational data up to and including CUI.
12_outputs: Large language model responses (prediction-based) following user prompting.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2023
19_date_acq_dev_began: 5/1/2023
20_date_implemented: 12/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: N/A – we are using existing pre-trained large language models, no training required.
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.
35_demo_features: Other
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: Argonne National Laboratory Information Technology Infrastructure
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: 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.
63_stakeholder_consult: None of the above
2_use_case_name: Topic Modeling for Energy.gov 
3_agency: Department of Energy
3_abr: DOE
4_bureau: PA HQ -  Office of Public Affairs (PA)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Minimize time spent through manual effort of reading and tagging 100,000 Energy.gov webpages.
12_outputs: A list of five tags that best categorize an Energy.gov webpage. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 5/15/2024
19_date_acq_dev_began: 7/16/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 89303019AIM000005
24_hisp_support: No
31_data_catalog: No
33_agency_data: Web content from Energy.gov is being used for 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.
40_has_ato: Yes
41_system_name: OCIO Google Cloud 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: 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.