GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: ServiceNow Virtual Agent
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: Searching for information using AI.
11_purpose_benefits: Provide troubleshooting assistance via chat to employees seeking assistance on IT-related issues

12_outputs: Chat responses

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 1/1/2024
20_date_implemented: 6/1/2024
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: Existing ticket data is used to train the model with data and training servers stored within FedRAMP High data centers where ServiceNow is hosted

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
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: ServiceNow ATO
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.
51_extension_request: No – Agency did not request an extension for this use case.
56_monitor_postdeploy: Automated and Regularly Scheduled Updates: Some aspects of the plan to monitor the AI system post-deployment are automated including re-training of models after detecting drift; however, data science teams are still significantly involved in the monitoring and re-deployment process.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
61_adverse_impact: No
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Databricks AI for Cloud Data Warehouse
3_agency: Department of Energy
3_abr: DOE
4_bureau: PNNL - Pacific Northwest National Laboratory (SC43 OIM)
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: Efficiency in analytics and deployment of AL/ML models
12_outputs: Recommendation based on analytic input 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
2_use_case_name: DOE Technical Standards
3_agency: Department of Energy
3_abr: DOE
4_bureau: EHSS HQ -  Office of Environment Health Safety & Security (EHSS)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Enhance quality and accuracy of technical standards, supporting DOE's commitment to safety excellence. 
12_outputs: Recommendations and feedback for improvement
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
24_hisp_support: No
28_iqa_compliance: Organizational-level business workflows integrates quality assurance procedures conducted by subject matter experts (SMEs) for all proposed responses and their publication(s).
29_contains_pii: No
31_data_catalog: No
37_custom_code: No
38_code_access: No – agency does not have access to source code.
39_code_link: Commercial Chatbots are used.No link provided.
40_has_ato: No
43_infra_provisioned: Other
45_compute_request: Other
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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: Standards must align with regulatory requirements, which are subject to updates that an AI chatbot might not capture effectively. AI chatbot might misinterpret technical terminology or regulatory requirements, requiring human review. 
59_question_type: Reasonable & Timely Notice that applies: In Person (used by program staff)
61_adverse_impact: No
65_appeal_process: No – it is not operationally practical to offer this.
67_opt_out: Other
67_question_type: The Chatbot(s) can alternately be used by the program team since they areoperated stand-alone.
2_use_case_name: Microsoft Copilot for Security
3_agency: Department of Energy
3_abr: DOE
4_bureau: PNNL - Pacific Northwest National Laboratory (SC43 OIM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Proactively identify, mitigate, and respond to threats. Copilot assists in real-time monitoring, threat detection, incident response automation, and vulnerability management.
12_outputs: The intended outputs of using Microsoft Copilot for Security at PNNL include real-time threat detection, automated incident response, enhanced data protection, compliance reports, security insights, reduced cyber risk, and sustained operational continuity
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Text To Speech Audio Generation
3_agency: Department of Energy
3_abr: DOE
4_bureau: KCNSC - Kansas City National Security Campus (KCFO)
8_topic_area: Education & Workforce
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: AI tool is used to generate audio files to be used in training content.Allows for faster development of training to meet company needs.  This leads to a cost savings in both initial development and future modifications. 
12_outputs: MP4 Audio Files
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
20_date_implemented: 9/16/2022
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: We are not training the AI model, project is COTS
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: N/A - System is used only for GREEN DATA, no OUO or CUI is allowed and does not require a System Security Plan to be named
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: No Risks Identified
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
61_adverse_impact: No
63_stakeholder_consult: Post transaction customer feedback collections
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: EnerGPT
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: EnerGPT aims to enhance user productivity and reduce time spent on redundant tasks . 
12_outputs: EnerGPT generates answers to user questions. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/8/2024
19_date_acq_dev_began: 7/18/2024
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: Publicly available government documentation was used to evaluate the model, an example document is the Administration Cybersecurity Priorities for the FY 2026 Budget PDF (https://www.whitehouse.gov/wp-content/uploads/2024/07/FY26-Cybersecurity-Priorities-Memo_Signed.pdf)  published by the White House. 
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: MI8 Collimators Surogate Model
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 project is working to create a ML surogate model of the exisitng MI8 collimation system. The purpose of the ML model is to aid in the tuning of the collimation system for acclerator operations and to help find more optimal settings in a timely manner. We hope to extend these techniques to other sub-systems and also a new MI8 collimation system being installed.
12_outputs: The ML outputs of the system are predictions of collimation system performance given collimation system settings and beam charecteristics.
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: 10/1/2024
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: Accelerator operations machine data
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 – source code is publicly available.
39_code_link: https://github.com/fermi-mi/collimators-ml
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: Records Digitization
3_agency: Department of Energy
3_abr: DOE
4_bureau: SEPA - Southeastern Power Administration (PMA)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Digitizing text from scanned documents or smart forms for archival purposes using AI.
11_purpose_benefits: SEPA is leveraging AI and ML to enhance metadata tagging and quality control during the records digitization process.
12_outputs: Acurate metadata assignment in accordance with SEPA's NARA-approved file plan/records schedule.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Machine Learning components within Splunk Enterprise Security
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: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Allows for big data analysis and identification of trends in large datasets. SLAC expects to be able to perform data clustering, trend analysis, classification, and regression by using Splunk

12_outputs: Prediction and clustering models

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2019
19_date_acq_dev_began: 8/1/2019
20_date_implemented: 9/1/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: SLAC-0000238366
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Splunk's machine learning model is trained on data generated by activity at SLAC

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: 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: Machine Learning components within CrowdStrike
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: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: CrowdStrike uses machine learning to review security events in order to create notifications of detections and incidents for the SLAC Cybersecurity team. From the use of CrowdStrike's machine learning components, SLAC receives the benefit of visibility to analyze possible security events

12_outputs: CrowdStrike Detections or Incidents

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/27/2021
19_date_acq_dev_began: 1/27/2021
20_date_implemented: 7/7/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: SLAC-0000208966

24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: CrowdStrike's machine learning model is trained on data generated by activity at SLAC

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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: CrowdStrike Falcon
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: AI Chat Bot for IT User Services
3_agency: Department of Energy
3_abr: DOE
4_bureau: PPPL - Princeton Plasma Physics Laboratory (SC43 OIM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Interactive RAG chat bot model trained on existing, updated and new IT resource documentation in the user space. Platform will be used as an informative method for users to handle tier 1 IT issues and help guide to the correct place.
12_outputs: AI output will be recommendations and instructions based on training data from IT administrators in more user friendly responses
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 9/1/2024
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: Help Desk Knowledge Base Article and other supporting documentation in the user space
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: 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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Planned or in-progress.
53_real_world_testing: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
54_key_risks: No key risks identified
55_independent_eval: Planned or in-progress
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.
59_question_type: Email
61_adverse_impact: No
62_disparity_mitigation: RAG Model will be significantly grounded in factual data, limiting creativity in responses
63_question_type: Independent feedback links will be given to receive feedback data outside of the model
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Tritium Accountancy for Fusione Energy
3_agency: Department of Energy
3_abr: DOE
4_bureau: SRS - SRNL - Savannah River Site - Savannah River National Laboratory (EM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Adaptive learning, improved uncertainty on accountancy, and prediction for commercial systems.
12_outputs: Real-time tritium data with reduced uncertaintingy for a continually operating fusion power plant. A framework to be used in full systems.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
22_dev_method: Developed in-house.
23_contract_piids: No-Cost
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Early Career Award from Office of Science; DOE Guidelines
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Sensor measurements of hydrogen and deuterium. Subject matter experts.
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: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: 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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
54_key_risks: Still working on AI algorithm assessment tests to identify optimal choice
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: Other, if other please explain in next question.
59_question_type: Not deployed and is still in development
61_adverse_impact: No
63_stakeholder_consult: Other
63_question_type: Subject matter expert discussion and stakeholder discussions during early development
65_appeal_process: Yes
67_opt_out: Yes