2_use_case_name: Interactive platform to help review and create "Promoting Inclusive and Equitable Research" Plans
3_agency: Department of Energy
3_abr: DOE
4_bureau: PPPL - Princeton Plasma Physics Laboratory (SC43 OIM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: A trained and informed model that can help to review PIER plans for accuracy, consistency and also to help integrate specific PPPL DEIA goals and initiatives, aligned with input from the user, helping to clearly define the goals of the research plan.
12_outputs: AI output will be revision suggestions to a user submitted PIER plan in order to align with PPPL specific goals and initiatives. It will also help to guide the user to create a more consistent plan with previously submitted/approved plans.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: PPPL specific PIER plan guidelines, previously submitted and approved PIER plans, public DOE guidance and other leadership data to help refine plans that align with laboratory strategic goals.
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
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: 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: Planned or in-progress.
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: No key risks identified
55_independent_eval: Planned or in-progress
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_question_type: Email
61_adverse_impact: No
62_disparity_mitigation: RAG Model will be significantly grounded in factual data, limiting creativity in responses
63_question_type: Independent feedback links will be given to receive feedback data outside of the model
65_appeal_process: Yes
67_opt_out: Yes