GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Github Copilot
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Collaborating in real-time using AI-assisted tools in word processors.
11_purpose_benefits: The intended purpose of GitHub Copilot is to assist developers by providing intelligent code suggestions and completions, thereby enhancing their productivity and efficiency.

Increased Productivity: By suggesting code snippets and completions, Copilot reduces the amount of time developers spend writing boilerplate code and looking up syntax or API usage.
Improved Code Quality: Copilot can suggest best practices and common patterns, helping developers write more robust and maintainable code.
Learning Aid: It serves as a learning tool for developers by providing examples and explanations of how to implement certain functionalities.
Reduced Cognitive Load: By handling repetitive coding tasks, Copilot allows developers to focus more on the logic and design of their applications.
Faster Prototyping: Developers can quickly generate code for new features or prototypes, accelerating the development process.
Overall, GitHub Copilot aims to streamline the coding process, making it faster and more efficient for developers to build high-quality software.
12_outputs: The AI system's outputs in this context would be:  Code Suggestions: The AI might suggest alternative ways to securely handle sensitive information like the master_key. For example, it could recommend using environment variables or secret management services instead of hardcoding the key. Security Warnings: The AI could provide warnings about potential security risks, such as the exposure of sensitive information in the code. Best Practices: The AI might suggest best practices for managing configuration settings, such as using a configuration file or a dedicated secrets management tool. Documentation: The AI could generate comments or documentation strings explaining the purpose of the master_key and how it should be used securely.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/30/2023
2_use_case_name: aPROMISE X
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf22/K220590.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
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: Other
31_question_type: Not needed for this device.
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: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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: Yes – Agency requested an extension for this use case.
2_use_case_name: AI coding
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Increased Productivity: By automating routine tasks, both coders and designers can work faster and more efficiently.
Improved Quality: AI can enhance the quality of code and designs through intelligent analysis and suggestions.
Accessibility: It can make coding and design more accessible to non-experts, democratizing creative processes.
Innovation: By offering new perspectives and solutions, AI can drive innovation in both fields.
Overall, the integration of AI into coding and creative design aims to enhance human capabilities, foster creativity, and improve overall outcomes in both areas.
12_outputs: Code Snippets: Automatically generated blocks of code based on user prompts or requirements. Bug Reports: Detailed analyses of code with identified issues and suggestions for fixes. Documentation: Automatically generated documentation for codebases, explaining functions, classes, and usage. Refactored Code: Improved versions of existing code that enhance readability, efficiency, or performance. Test Cases: Suggested test cases or automated tests generated to ensure code functionality and reliability.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: Aplio i900, Aplio i800 and Aplio i700 Software V8.1 Diagnostic Ultrasound System
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf23/K233195.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
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: Other
31_question_type: Not needed for this device.
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: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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: Yes – Agency requested an extension for this use case.
2_use_case_name: Alignment System Cranial, Alignment Software Cranial, Cirq Alignment Software Cranial Biopsy, Cirq Alignment Software Cranial sEEG, Varioguide Alignment Software Cranial
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf22/K223864.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
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: Other
31_question_type: Not needed for this device.
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: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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: Yes – Agency requested an extension for this use case.
2_use_case_name: Dental - Dentsply Sirona CERE
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Intelligent automation enables a fast proposed dental reconstruction proposal based on the scan. 
12_outputs: Dental reconstruction proposal.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Fusion 2024 (JAWS/ZoomText)
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Allows blind and low vision employees to gain information from charts, graphs, tables and other images on the screen to provide comparable access in support of legal requirements of Section 508.
12_outputs: Detailed descriptions and details of charts, graphs, tables and other images on the screen. It also allows for follow on questions to obtain additional information such as trends, specific details, etc.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/2/2024
19_date_acq_dev_began: 5/2/2024
20_date_implemented: 6/25/2024
40_has_ato: Yes
41_system_name: Section 508 Tools ID: 1333
2_use_case_name: Acumen Hypotension Prediction Index - EV1000 Clinical Platform, Acumen Hypotension Prediction Index - Hemosphere Advanced Monitoring Platform, Acumen Hypotension Prediction Index, Hemosphere Advanced Monitoring Platform - Pressure
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf18/K183646.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
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: Other
31_question_type: Not needed for this device.
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: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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: Yes – Agency requested an extension for this use case.
2_use_case_name: Xtract WDS - OSSO
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Law & Justice
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Heat map detection for weapon detection software.
12_outputs: Areas of interest for a heat map that include objects that would produce heat indicators aligned with heat signatures of known types of different weapons.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 1/26/2023
19_date_acq_dev_began: 1/26/2023
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
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: 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: 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: 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: Transcription Services
3_agency: Department of Veterans Affairs 
3_abr: VA
4_bureau: OIG
8_topic_area: Mission-Enabling
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Increased efficiency
12_outputs: transcribed meetings and interviews
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 7/1/2024
20_date_implemented: 7/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: Https://www.noopensourcedotherusingAzureservices.com
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: 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: Planned or in-progress.
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.
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
67_opt_out: Yes
2_use_case_name: Chatbot within ESMS
3_agency: Department of Veterans Affairs 
3_abr: VA
4_bureau: OIG
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Increased efficiency
12_outputs: internal facing, chatbot to assist with policy questions
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/25/2024
19_date_acq_dev_began: 12/2/2024
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: Current policies and SOPs
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
41_system_name: Ivanti
43_infra_provisioned: Yes
45_compute_request: 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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Planned or in-progress.
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
67_opt_out: Yes
2_use_case_name: Extreme Load Analysis
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Enteprise Planning
8_topic_area: Energy & the Environment
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Extreme load analysis involves estimating the likelihood of peaks in energy demand for different months of the year and hours of the day. This will aid in energy forecasting
12_outputs: An RShiny Web App displays EVD for different months of the year, hours of the day, and plants. The Web App allows users to filter for different time periods and different plants. Users can also adjust
percentiles to see different quantile values.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/30/2024
19_date_acq_dev_began: 1/30/2024
20_date_implemented: 5/1/2024
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: Weather Data: Going back to 1960.
ELRF Data: Going to back to 1980.
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: 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: 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: Yes
53_real_world_testing: Yes
54_key_risks: Model accuracy, drift
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 Intelligent Catalog Assistant
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Supply Chain   
8_topic_area: Energy & the Environment
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Transform how inventory is viewed in catalog. Provide intelligent search capability to quickly find what user needs. Reduce duplication of inventory ordering due to inventory and components
12_outputs: Intelligent Search engine with filters to search through inventory. Also provides information on item, location, cost, and identifies like or similar items
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/1/2019
19_date_acq_dev_began: 12/1/2019
20_date_implemented: 9/30/2020
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 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
54_key_risks: Model accuracy, data quality
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