2_use_case_name: INL AI Virtual Assitant (AiVA)
3_agency: Department of Energy
3_abr: DOE
4_bureau: NE INL - NE Idaho National Laboratory (NE)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: This chatbot uses commercial chatgpt-like capability to answer questions, provide coaching on processes, summarize and improve communications, and produce code in a variety of formats.  INL has been authorized and has planned activiites in 2025 to begin adding internal INL non-CUI data using RAG.  Examples include the employee handbook and approved controlled documents.
12_outputs: Outputs are consistent with commercial chatbot products, such as ChatGPT.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 1/1/2024
20_date_implemented: 5/13/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: At this time, INL plans to use non-CUI data with this solution, including the employee handbook, approved controlled documents, and other material that will assist workers in completing processes and activities.  RAG (mini RAG preferred) is the method that will be used for integration with the AI solution.
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: CS-SSP-123,Cybersecurity System Security Plan for Azure Platform
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: Key functional risks are similar to use of ChatGPT, including fabricated & inaccurate answers, model & output bias, and potential copyright risks.  Mitigated operational risks include data privacy, fraud and exploit risks.  Additional considerations include ethics and professional responsibilities, as associated with any new technology/process differentiator.
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: In person 
61_adverse_impact: No
62_disparity_mitigation: Not using PII, models and interface are commercial
63_stakeholder_consult: None of the above
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: This is an internally used use case.  Primary feedback mechanisms are direct use testing and customer feedback.
67_opt_out: Yes