GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Groundwater Modeling
3_agency: Department of Energy
3_abr: DOE
4_bureau: LM HQ - Office of Legacy Management (LM)
8_topic_area: Science & Space
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Predicts flow of ground water containments underground.
12_outputs: Parameter estimation for return to groundwater models
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2003
19_date_acq_dev_began: 2/1/2003
20_date_implemented: 10/1/2003
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids:  IDIQ Contract # 89303020DLM000001.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Parameters and model outputs are reviewed and validated by a subject matter expert
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Data for this use case are stored in a single repository, but it is not yet enterprise-wide data.
33_agency_data: LM maintains historically collected groundwater data to train the model.
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: No
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link: https://pesthomepage.org/programs
40_has_ato: Yes
41_system_name: Legacy Management General Support System
2_use_case_name: OpenText for Records Management (Email 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 email records.
12_outputs: OpenText will categorize each email message to an appropriate category.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 6/1/2023
2_use_case_name: Applications of Natural Language Processing and Similarity Measures for Similarity Ranking
3_agency: Department of Energy
3_abr: DOE
4_bureau: EHSS HQ -  Office of Environment Health Safety & Security (EHSS)
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: (1) Demonstrate Capabilities of AI (2) Cost savings by sharing algorithms, approaches and lessons learned of those evaluated and deployed in the ES&H DAMaL Tools.
12_outputs: Custom output (e.g., ranking of records)
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 10/1/2021
20_date_implemented: 10/1/2021
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: Approach is NOT supervised ML.
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.
39_code_link: Code is based on open source approaches. Link to open source code is not provided.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Other
47_question_type: All algorithms, code, training, is developed and tested in a desktop and Python environment and if successful then deployed under a contract managed MS Azure environment.
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.
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.
55_independent_eval: Planned or in-progress
61_adverse_impact: No
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: System runs locally and run on a desktop in a local Python enviornment.
67_opt_out: Other
67_question_type: System runs locally and run on a desktop in a local Python enviornment.
2_use_case_name: Fast AI for real-time/online diagnostics and calibration; AI-based autonomous system control/operation optimization (including anomaly/failure detection)
3_agency: Department of Energy
3_abr: DOE
4_bureau: SLAC - SLAC National Accelerator Laboratory (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: AI/ML to improve efficiency and quality of accelerator-based scientific user facility operation, including for light sources. Benefits include higher science throughput in scientific user facilities, greater energy efficiency, new scientific capabilities at Office of Science user facilities.

12_outputs: Prediction of acclerator beam behavior, decisions about controllable variables to adjust to improve beam behavior.

16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2019
19_date_acq_dev_began: 10/1/2019
20_date_implemented: 9/30/2023
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: No
33_agency_data: Training data are collected from physics simulations and the accelerator as it runs.

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 – source code is publicly available.
39_code_link: https://github.com/xopt-org
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: 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: AI for system design optimization (e.g., detector, accelerator)
3_agency: Department of Energy
3_abr: DOE
4_bureau: SLAC - SLAC National Accelerator Laboratory (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: AI/ML models a relatively small data sets in a high dimensional parameter space and provides accurate predictions of future data points. The prediction capability in turn improves the convergence efficiency of the optimization algorithm, which is used for time consuming design optimization.

12_outputs: The AI system is a non-parametric data fitting model and its output is predicted function values of any new solution in the parameter space and the uncertainties. 

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2017
19_date_acq_dev_began: 10/1/2017
20_date_implemented: 9/30/2020
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: Training data are collected at the time of running the optimization algorithm. 

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: No
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/SPEAR3-ML/MG-GPO
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: 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: PARSGPT
3_agency: Department of Energy
3_abr: DOE
4_bureau: PM HQ -  Office of Project Management (PM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: The value-add is derived from providing an accessible way for PM Analysts to safely interact with LLM technology.
12_outputs: Free form text response to questions (Chatbot)
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/18/2023
19_date_acq_dev_began: 8/23/2023
20_date_implemented: 9/10/2023
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
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: Project Assessment and Reporting System (PARS)
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: CrowdStrike
3_agency: Department of Energy
3_abr: DOE
4_bureau: LM HQ - Office of Legacy Management (LM)
8_topic_area: Other
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Predicts malicious behavior, provides decisions and blocks malicious behavior.
12_outputs: Prediction, Recommendation, Decision
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/15/2023
19_date_acq_dev_began: 6/15/2023
20_date_implemented: 6/15/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Not Applicable
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: LMs client and server logs data set
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.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Legacy Management General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: We are using pre-existing LM resources.
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.
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: Privacy Loss
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: Yes - All individual decisions or actions are automated and may be rendered without direct human interaction.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: Manual Review
63_stakeholder_consult: None of the above
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: AI use case does not impact public
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.
2_use_case_name: ServiceNow Predictive Intelligence
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: Prioritizing and categorizing incoming emails using AI.
11_purpose_benefits: Provide better and more consistent classification of ticket data entered into ServiceNow

12_outputs: Field classification data

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
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: 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
37_custom_code: No
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.
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.
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: AI-Enhanced Lab Assist
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: Logging and analyzing time spent on tasks using AI-powered time management tools.
11_purpose_benefits: Integrating lessons learned into Lab Assist Activity Planning to enhance operational efficiency, improve information sharing leveraging best practices, and foster a culture of continuous improvement.  
12_outputs: Leverage AI for trend detection in working planning and control data.
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Microsoft 365 Copilot (Productivity Suite)
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: Improving the quality of written communications using AI tools.
11_purpose_benefits: This helps PNNL streamline workflows, improve efficiency, and allow researchers to focus more on innovation and less on administrative tasks, ultimately accelerating scientific research and operational effectiveness.
12_outputs: Using Microsoft 365 Copilot, PNNL aims to produce enhanced document quality, increased efficiency, insightful data analysis, improved collaboration, automated workflows.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
2_use_case_name: NLCOO AI for Lessons Learned tool 
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: Better search to gain insights from exisitng lessons learned to improve how we do work.

12_outputs: Search list of relevant documents

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/27/2024
19_date_acq_dev_began: 3/16/2024
20_date_implemented: 3/16/2024
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 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.
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: No
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.
51_extension_request: No – Agency did not request an extension for this use case.
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
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name:  Spot for automated sensing, inspection, and capture
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: Spot, a mobile robot, can navigate hazardous or hard-to-reach areas to perform inspections and gather precise data with its advanced sensors and cameras.
12_outputs: Improving efficiency, and the accuracy of sensor research data.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
2_use_case_name: Microsoft Power Platform capability that provides AI to automate processes in Power Apps and Power Automate
3_agency: Department of Energy
3_abr: DOE
4_bureau: PNNL - Pacific Northwest National Laboratory (SC43 OIM)
8_topic_area: Other
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: By leveraging AI for automation, PNNL can automate routine tasks such as data entry, reporting, and workflow management, freeing up researchers and staff to focus on higher-value activities. 
12_outputs: Enhances productivity, reduces human error, and leads to more efficient management of research projects and resources.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
2_use_case_name: CAISY
3_agency: Department of Energy
3_abr: DOE
4_bureau: HC HQ -  Office of the Chief Human Capital Officer (HC)
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: 1. CAISY provides interactive, scenario-based content powered by AI where a learner can practice new skills in a safe space. This AI simulation content allows learners to choose a role, practice specific skills by responding to AI prompts, and receive adaptive, personalized feedback to guide their development
2. The user  is introduced by an avatar that is generated with AI text-to-video. After the introduction, the learner can either interact by typing or using speech-to-text (STT) and text-to-speech (TTS) services for more immersive and natural interaction.
When the conversation is over, the learner will receive a rating and evaluation

12_outputs: speech-to-text (STT) and text-to-speech (TTS) services for more immersive and natural interaction.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/4/2024
19_date_acq_dev_began: 8/21/2024
20_date_implemented: 8/21/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: None of the above
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: PERCIPIO/CAISY
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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.