2_use_case_name: OPQ-AI
3_agency: Department of Energy
3_abr: DOE
4_bureau: ANL - Argonne National Laboratory (SC43 OIM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: None of the above.
11_purpose_benefits: Significantly reduces person-hours for manual review of incoming people registrations to match with existing database records by recommending most likely matches, if an existing record is identified that matches with the registration details.
12_outputs: Recommendation of matched person record that already exists or that a new person record should be created.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/1/2019
19_date_acq_dev_began: 5/1/2023
20_date_implemented: 12/1/2023
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: Yes
33_agency_data: Data includes read-only access internal person/HR records existing in current database systems.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Argonne National Laboratory Information Technology Infrastructure
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: No
53_real_world_testing: Impact evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution and has utilized randomized experiments with a control group or other counterfactual, social systems analysis, or other rigorous research methodologies to evaluate impact and identify potential harm to users as well as broader groups of people.
54_key_risks: Mis-match of person records, requires human intervention to re-assign records or remove incorrect records.
55_independent_eval: Yes – by an agency AI oversight board 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: Other, if other please explain in next question.
59_question_type: The user does interact directly with OPQ-AI. OPQ-AI is a back office process.
61_adverse_impact: No
62_disparity_mitigation: None. No demographic disparities are forseen.
63_stakeholder_consult: Other
63_question_type: Feedback from software development teams for enhancements to response from AI use case.
65_appeal_process: Yes
67_opt_out: Other
67_question_type: The AI functionality is solely used for the prevention, detection, and investigation of duplicate registration records.