2_use_case_name: Hanford  Service Ticket Lookup
3_agency: Department of Energy
3_abr: DOE
4_bureau: RL - Hanford - Richland Operations Office - Hanford (EM)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The purpose of the Hanford Service Ticket Lookup is to provide a single interface with for customers to ask questions and get to service tickets without having to navigate extensive menu's, tool bars, and search functions. Eventually, this will include service tickets from multiple platforms, providing the customer with a single interface to do all things service request related. Additionally, users can ask questions in natural language instead of needing to input specific search criteria.  

12_outputs: The system outputs text respones from user prompts requesting information on grounded data related to Service Ticket Requests

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 5/7/2024
19_date_acq_dev_began: 6/22/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 89303320DEM000031

24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Our Data does not train the models.

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: Microsoft Azure OpenAI
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
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: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
54_key_risks: Risks identified in this AI were incorrect or halucination responses from the AI. Users are trained and informed to validate the AI responses before use. They were identified through discimination of information of risk from other ChatGPT applications along with developer and user testing.

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_ai_notice: Other, if other please explain in next question.
59_question_type: HMIS is collaborating with, and recieiving approval from, our local DOE on our internal communication plans. Once our internal developement is ready for customer deployment and communications, HMIS will provided their use cases to all users via an AI website where users can view and request use cases. When a use case gets developed, those effected will be communication with about the AI, and provided informational material, training, and feedback loops to provide reasonable and timely notices. Reasonable and Timely notices include Email, Team Channel,  and Online.

61_adverse_impact: No
62_disparity_mitigation: The AI does not take into account any demographic information when generating responses, and does not use any demographic information about employees specifically to generate results. Our AI use case should have have any demographic disparity. If a users were to find an issue with a response, they can provide feedback via a feedback mechanism with each response and provide a summary of their feedback. Developers would then address the issue and look to mitigate any issues found.

63_stakeholder_consult: Other
63_question_type: The development of our AI was done with the consultation of our affected community, where affected community is the community of users who would be using the AI, as well as the community of DOE sites that have employed similar AI tools. We have consulted individually and in groups our community and provided early demonstration, feedback mechanisms, direct user testing, post-transaction feedback collection. Areas (Team Channels) where listening and meetings on topics can occur will be provided before release.

65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: There currently is no escalation process fro the AI use case to evaluate the impact on the individual or group. Use of the AI is a productivty use and is voluntary to the users. Users who do not want to use the AI are not required and any issues personally that arise with an individual or group related to the AI can be captured via our feedback, help, and AI Team communication channels and will be reviewed by IMS Leadership.

67_opt_out: Yes