GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Vegetation Management
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Right of Way
8_topic_area: Energy & the Environment
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Monitoring of vegetation around  transmission line and tower and predictive analytics on when/where to trim. Using NV5 to generate the reports
12_outputs: Report on areas identified where vegetation needs to be trimmed
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: image 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.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
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: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Established Process of Machine Learning Operations: Alongside automated testing and drift detection, model re-training and re-deployments are supported by continuous integration pipelines that are managed by machine learning and data engineers on the platform, adapting work done by data science team into repeatable scripts for re-training and re-testing a model once deployed.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: CAP Automation
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Enterprise Analytics & Innovation
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: Use NLP to classify condition reports as C, E, or WO level
12_outputs: A classification label - C, E, or WO
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/19/2023
19_date_acq_dev_began: 5/19/2023
20_date_implemented: 4/24/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Condition reports
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: Python application
42_dev_tools_wait: More than 12 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: Yes
53_real_world_testing: Yes
54_key_risks: Model accuracy. 
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: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: CR intelligent search
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Enterprise Analytics & Innovation
8_topic_area: Energy & the Environment
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Use NLP to search through CRs. Also tool to include pattern recognition and adding additional safety data to search for and do topic modeling on
12_outputs: Search engine for looking through condition reports
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/15/2021
19_date_acq_dev_began: 6/15/2021
20_date_implemented: 3/31/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Condition reports
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: Python Dash
42_dev_tools_wait: 6-12 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: Yes
53_real_world_testing: Yes
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: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Materials tracking
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Supply Chain   
8_topic_area: Energy & the Environment
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Use predictive analytics and confidence levels to track materials as they go through the supply chain process and risk on being delivered on time
12_outputs: Dashboard for monitoring where in the supply chain process, item is and if it is at risk for being late
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/5/2020
19_date_acq_dev_began: 1/5/2020
20_date_implemented: 5/1/2021
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Maximo 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.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: MS Azure, PowerBI
42_dev_tools_wait: More than 12 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: Yes
53_real_world_testing: Yes
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: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Heliviewer
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Enterprise Analytics & Innovation
8_topic_area: Energy & the Environment
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Use of helicopter image data for survey and project insights
12_outputs: Search engine of helicopter data to search through images, projects, flight patterns, etc.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/22/2022
19_date_acq_dev_began: 1/22/2022
20_date_implemented: 7/20/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Helicopter images
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: MS Azure
42_dev_tools_wait: More than 12 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: Yes
53_real_world_testing: Yes
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: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: ICI camera 
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Enterprise Analytics & Innovation
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: Use computer vision to detect rate of change of water temperature with thermal image data which is indicative of dam leakage
12_outputs: Predictions on what temperature of water should be and risk flag if over threshold
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/5/2020
19_date_acq_dev_began: 11/5/2020
20_date_implemented: 8/15/2021
21_date_retired: 10/15/2021
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Thermal camera imagery
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
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: Yes
53_real_world_testing: Yes
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: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: GET
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Generation Projects & Fleet Strategy
8_topic_area: Energy & the Environment
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Groundwater evaluation Tool - The GET Tool is designed to provide statistical analysis of groundwater monitoring data required under State regulatory programs and the federal Coal Combustion Residuals (CCR) rule. It uses state-of-the-art statistical and predictive methods to evaluate geochemical and geotechnical data under three phases of groundwater monitoring: Detection, Assessment, and Corrective Action. In “Detection”, background data is used to construct prediction limits for use in identifying statistically significant changes from background levels. In “Assessment” and “Corrective Action”, compliance data is used to compute confidence intervals and confidence bands to assess violations of groundwater protection standards (GWPS) to inform of corrective action needs.
12_outputs: Dashboard and reports of statistical analysis of groundwater data
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/1/2021
19_date_acq_dev_began: 12/1/2021
20_date_implemented: 7/21/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Groundwater data from Equis
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: More than 12 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: Yes
53_real_world_testing: Yes
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: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: GMET
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Generation Projects & Fleet Strategy
8_topic_area: Energy & the Environment
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: The Groundwater Modeling Evaluation Tool (GMET) is an R Shiny application that uses Phreeqc geochemical models and analysis to evaluate groundwater chemistry data at each of TVA’s CCR sites 
12_outputs: Geochemical reports on chemical makeup of groundwater data
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/23/2023
19_date_acq_dev_began: 7/23/2023
20_date_implemented: 2/6/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Groundwater data from Equis
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: 6-12 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: Yes
53_real_world_testing: Yes
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: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Optiwatt EV Load Model
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: Linear model to predict hourly unitized charging load from non-fleet EVs in a typical meteorological year (TMY).
12_outputs: Hourly load shape profiles for Evs
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/22/2024
19_date_acq_dev_began: 1/22/2024
20_date_implemented: 2/23/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Optiwatt charging data, ELRF weather, TMY Weather
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: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
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: Yes
53_real_world_testing: Yes
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: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes