2_use_case_name: ServiceNow Now Assist
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 transactional service desk requests through improved incident search, resolution, feedback as well as AI-assisted coding of  workflows.
12_outputs: Outputs are consistent with commercial service desk products, including the search, creation and closure of service requests.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: This use case will utilize service desk incident, problem and knowledge management sources for training and use.
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-CAR-157, Cybersecurity System Security Plan for ServiceNow Platform
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
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 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: Using minimal PII to identify employee (name, id#), 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