2_use_case_name: Scopus AI
3_agency: Department of Energy
3_abr: DOE
4_bureau: NE INL - NE Idaho National Laboratory (NE)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Assist researchers by reducing time to find applicable research while increasing quality and accuracy of identified hits.
12_outputs: Research citations, abstraacts and other summaries.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 8/6/2024
20_date_implemented: 8/6/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Scopus AI was purcahsed, added as part of the Elsevier/Scopus subcontract.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: None. Scopus AI uses publicly available journal abstracts, no agency data is used.
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.
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: Other
43_question_type: Developed by Elsevier/Scopus with data publicly, but available journal articles. Some journals are copywritten.
45_compute_request: Other
45_question_type: We will not be training the model.
47_timely_resources: Other
47_question_type: Not applicable.
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 developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
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 data privacy, fraud and exploit risks.
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
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: In person 
61_adverse_impact: No
62_disparity_mitigation: Not using PII, 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