2_use_case_name: AI for Intelligent Automation
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: None of the above.
11_purpose_benefits: Improve the timeliness and quality of manual work processes through automation where generative AI can make comparisons and decisions using INL procedures and controlled documents, with humans performing final validation and approval.
12_outputs: Completion of forms for human validation and approval.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
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: 6-12 months
43_infra_provisioned: Other
43_question_type: This AI will be utilized by applications and not directly by individuals.
45_compute_request: No
47_timely_resources: Other
47_question_type: Project in initiation phase.
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: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
54_key_risks: Key functional risks include fabricated & inaccurate answers as well as model & output bias.  Mitigated operational risks include model exploit.
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: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: Using minimal sensitive information, as identified in ATU, built on commercial solution
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