GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: In-Situ Monitoring of Additively Manufactured Parts
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: Identify print defects during the printing process.
12_outputs: Layerwise annotated images of defects in print bed that can be registered to post print analysis or trigger a fixing process or a full stop to save on material.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 10/1/2022
20_date_implemented: 10/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: Government use license
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Print data
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: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: ORNL Software
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.
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: 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: Annotation
55_independent_eval: Planned or in-progress
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.
59_ai_notice: Email , Other, if other please explain in next question.
59_question_type: Email and regularly scheduled meetings.

61_adverse_impact: No
63_stakeholder_consult: Other
63_question_type: N/A ORNL Software
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Boston Dynamics Spot Robotics
3_agency: Department of Energy
3_abr: DOE
4_bureau: Y-12 - Consolidated Nuclear Security Y-12 (NPO)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Automation of the robots movements
12_outputs: Development and testing of robotic use for security and emergency responses, helping to decide on the "best" path for the robot to move
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/10/2021
19_date_acq_dev_began: 10/1/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: PO 4300174099 (renewed annually)
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
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: 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: No IT Infrastructure needed
45_compute_request: Other
45_question_type: Not Required
47_timely_resources: Other
47_question_type: Not Required
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: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: Public acceptance of robotics integration
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
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.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
63_stakeholder_consult: Direct user testing, Post transaction customer feedback collections
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Elastic Stack Technology (ELK)
3_agency: Department of Energy
3_abr: DOE
4_bureau: Y-12 - Consolidated Nuclear Security Y-12 (NPO)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Enable intelligent media cataloging to assist with data discovery
12_outputs: Intelligent collection content searching using ElasticSearch, Logstash, and Kibana
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 12/1/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: PO 4300182430 (renewed annually)
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Knowledge Preservation Management (KPM) Media
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: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Elastic Stack Software
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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: Not identifying the right media for target searches
55_independent_eval: Planned or in-progress
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.
59_ai_notice: Online – in the terms or instruction for the service
61_adverse_impact: Yes
62_disparity_mitigation: Manual review by Human in the Loop resolves discrepancies
63_stakeholder_consult: Direct user testing, Post transaction customer feedback collections
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Raytheon Multimedia Monitoring System (M3S)
3_agency: Department of Energy
3_abr: DOE
4_bureau: Y-12 - Consolidated Nuclear Security Y-12 (NPO)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Enable off-cloud transcription of pre-recorded video media to improve data discoverability
12_outputs: Off-cloud voice to text transcription technology for pre-recorded videos
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2017
19_date_acq_dev_began: 12/1/2017
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: PO 4300183978 (renewed annually)
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Knowledge Preservation Management (KPM) Media
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: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: The Multimedia Monitoring System
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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: Incomplete or incorrect transcription of videos, negatively impacting the discoverability of this media
55_independent_eval: Planned or in-progress
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.
59_ai_notice: Online – in the terms or instruction for the service
61_adverse_impact: Yes
62_disparity_mitigation: Manual review by Human in the Loop resolves discrepancies
63_stakeholder_consult: Direct user testing, Post transaction customer feedback collections
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Cognitive Prescreen Tool (CPT)
3_agency: Department of Energy
3_abr: DOE
4_bureau: Y-12 - Consolidated Nuclear Security Y-12 (NPO)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Serve as a recommender to Derivative Classifier to assist with document review to improve process accuracy and efficiency, in that order.
12_outputs: Sensitive information detection bound to DOE classification guidance to help reduce IOSC and prevent information loss
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2016
19_date_acq_dev_began: 10/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: No
33_agency_data: Classification Guides from the CNS Classification Office
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: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Automated Classification System
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: 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.
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: IOSC potential as information detection models are tuned
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
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_ai_notice: Online – in the terms or instruction for the service
61_adverse_impact: Yes
62_disparity_mitigation: Manual review by Human in the Loop resolves discrepancies
63_stakeholder_consult: Direct user testing, Post transaction customer feedback collections
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Machine Learning for Linac Improved Performance
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: Daily fluctuations in the Ion Source conditions as well af the effect of environmental changes to RF systems and cavities affect Linac beam. Results include increased beam loss resulting in increased beamline component irradiation, decreased beam intensity to downstream machines affecting Accelerator Complex deliverables, drifts in Linac beam energy directly affecting Booster losses. These drifts are not easily predictable since we do not have environmental control on the RF gallery, not enough instrumentaion in the Ion Source or Linac proper. To counter these effects, we are developing AI-based optimization and modeling, including Bayesian Optimization and surrogate model-based optimization, with the ultimate goal of (near) real-time RF compensation.
12_outputs: Outputs are proposed changes to RF system parameters (cavity phase settings and/or field gradients) to counter the effect of daily drift and to stabilize the output energy.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2020
19_date_acq_dev_began: 10/1/2020
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 as well as accelerator simulation
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: Next-Generation Beam Cooling and Control with Optical Stochastic Cooling
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 enhanced real-time control of the structure of circulating particle beams. The additional performance and capabilities provided may enable substantially greater operational flexibility and science reach at current and future DOE accelerator facilities.
12_outputs: The AI system will continuously infer the state of a circulating beam distribution and then use this inference in the execution of an RL-based control policy. The primary means of control is an advanced optical stochastic cooling system.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 10/1/2021
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: Large-scale simulation data is being used to train the diagnostic and control systems. Online training with experimental data may also be leveraged once the system is operational.
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: high level synthesis for machine learning (previously hls4ml)
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: hls4ml is used to implement specialized AI algorithms in embedded hardware.  This is valuable across a wide range of scientific applications for enabling real-time processing capabilitles.  This can accelerate sientific discovery and time to science thus enabling large cost savings and DOE scientific prestige.
12_outputs: It can be an AI algorithm from prediction to data compression to control (decision making).
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 6/1/2021
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: research datasets from scientific experiments
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: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/fastmachinelearning/hls4ml
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: Streamining intelligent detectors for sPHENIX/EIC
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: AI tools are developed for embedded inference in real-time processing systems for scientific experiments  such as sPHENIX and upcoming EIC. This can accelerate sientific discovery and time to science thus enabling large cost savings and DOE scientific prestige.
12_outputs: It can be an AI algorithm from prediction to data compression to control (decision making).
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 10/1/2021
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: 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