2_use_case_name: Hanford Ai Liaison
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: None of the above.
11_purpose_benefits: The purpose of the Hanford Ai Liaison tool is to provide a prompt to text tool that will allow users to ask questions against the chat and receive generative responses based on model training. The benefits of this AI are that it provides a mitigation for users that were using similar funtionality in external ChatGPT tools that were available to AI users and then blocked. It also provides a safe, secure enviroment for a ChatGPT experience where our prompts will not be used to train models in external system and our prompt and document data does not leave our accedidation boundary.
12_outputs: The system outputs text respones from user prompts using generative, pre-trained, transformer logic large language modeling (GPT LLM)
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/23/2024
19_date_acq_dev_began: 5/14/2024
20_date_implemented: 10/28/2024
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: No
33_agency_data: No agency-owned data is used in this model. Our Data does not train the models.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups.
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: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders 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: 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: 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: Yes – by another appropriate agency office that was 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: HMIS is collaborating with, and recieiving approval from, our local DOE on our internal communication plans. HMIS has 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 are 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, and areas (Team Channels) where listening and meetings on topics can occur.
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