2_use_case_name: SCMC AWS Data Infrastructure
3_agency: Department of Energy
3_abr: DOE
4_bureau: KCNSC - Kansas City National Security Campus (KCFO)
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: Automation of multiple manual processes and improvement of data quality and governance.
12_outputs: Initially the system will use M.L. to identify  inconsistent data and make preditions of how that data should be organized. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Procured under SCMC Agreement # WS181016
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non production related procurement data is currently being collected and managed by the SCMC. This is the data set that wil be used by the system.
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: Yes
41_system_name: AWS US EAST/WEST 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
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: Not applicable to the risks defined above
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: Other, if other please explain in next question.
59_question_type: Not applicable to the definitions defined above
61_adverse_impact: No
62_disparity_mitigation: Not applicable to the scenario defined above
63_stakeholder_consult: Other
63_question_type: Not applicable to the scenario defined above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: There is no AI system outcome that would have an impact on any specific individual or group
67_opt_out: Yes