2_use_case_name: Data Analytics and Machine Learning (DAMaL) Tools to enhance the analysis of Environment, Safety and Health (ES&H) data:  Unsupervised Machine Learning Text Clustering
3_agency: Department of Energy
3_abr: DOE
4_bureau: EHSS HQ -  Office of Environment Health Safety & Security (EHSS)
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: (1) Grouping Reports using AI (i.e., Clustering) (2) Improve efficiency of grouping and highlighting important data (e.g., ES&H data)
12_outputs: Clustering of records with top terms and topics
16_dev_stage: Operation and Maintenance
17_impact_type: Safety-impacting
18_date_initiated: 8/1/2018
19_date_acq_dev_began: 8/1/2018
20_date_implemented: 8/1/2018
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: GSA Contract #: GS-00F-0004T 
Contract ID # Support to EHSS: 89243321FAU400002
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: ES&H Data (e.g., ORPS, CAIRS, FPRS, DOE OPEXShare). Approach utilized is Unsupervised ML
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.
39_code_link: Code is based on open source approaches but open source code link not provided
40_has_ato: Yes
41_system_name: Reporting and  Analytical System (RAS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Other
47_question_type: All algorithms, code, training, is developed and tested in a desktop and Python environment and if successful then deployed under a contract managed MS Azure environment.
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 published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: Key risk from using the AI is providing misleading information to ES&H practitioners and subject matter experts.
55_independent_eval: Planned or in-progress
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: Email 
59_question_type: Tools available to all DOE Employees but require direct request to the Development team. Various communications (e.g., welcome email, tools login landing page, demos) and documentation (e.g., users guide) include extensive details on how the tools use AI as part of its dashboards.
61_adverse_impact: No
63_question_type: The system supports and affect DOE employees that use the system. The ES&H DAMaL Tools have various processes in place to consult and incorporate feed back (e.g., email, demos, training, user testing, feedback form, etc.).
65_appeal_process: Yes
67_opt_out: Yes