GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Intelligent Superfund Search & Chatbot capabilities
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Land and Emergency Management
8_topic_area: Energy & the Environment
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The intended purpose of this AI use case is to modernize and enhance the EPA's Superfund Enterprise Management System (SEMS) document search and processing system through intelligent automation. Key expected benefits include:

1. Dramatic improvement in operational efficiency by reducing search time from months/weeks to minutes
2. Enhanced accuracy in document retrieval and information extraction
3. Better user experience through intuitive natural language search capabilities
4. Cost reduction through automated document processing
5. Improved compliance and quality control in environmental site management
6. Increased ability to process and derive insights from millions of technical documents
7. Better decision support for EPA staff managing contaminated sites

12_outputs: The AI system will generate the following outputs:

1. Intelligent Document Processing Results:
   - Extracted key entities (site locations, contaminants, responsible parties)
   - Document classifications and metadata
   - Automated summaries of technical reports

2. Search and Retrieval Outputs:
   - Relevant document recommendations based on natural language queries
   - Direct answers to specific questions with source citations
   - Cross-document insights and connections

3. Analysis and Decision Support:
   - Site contamination pattern analysis
   - Related case recommendations
   - Risk assessment summaries

4. Quality Assurance Outputs:
   - Confidence scores for extracted information
   - Source attribution for all provided information

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2024
19_date_acq_dev_began: 4/1/2024
31_data_catalog: No
33_agency_data: No data is used for AI training, finetuning. Subset of agency records is used for evaluation.
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.
40_has_ato: Yes
41_system_name: Superfund Enterprise Management System
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
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.
2_use_case_name: Resource Conservation and Recovery Act (RCRA) Predictive Analytics
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Enforcement and Compliance Assurance
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Risk scoring of Large Quantity Generators (LQGs) to support RCRA inspections

12_outputs: Risk scoring (integers 0 to 4, inclusive) for LQGs

16_dev_stage: Implementation and Assessment
17_impact_type: Safety-Impacting
18_date_initiated: 7/31/2019
19_date_acq_dev_began: 7/31/2019
20_date_implemented: 10/12/2022
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: From https://echo.epa.gov/tools/data-downloads : Facility Registry Service, Air Stationary Source Data Downloads, Water National Pollutant Discharge Elimination System (NPDES) Data Downloads, Water Biosolids Data Download, Hazardous Waste Data Download
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.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Data Management and Analytics Platform (DMAP)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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: Yes – Agency requested an extension for this use case.
2_use_case_name: Clean Water Act (CWA)Predictive Analytics
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Enforcement and Compliance Assurance
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Identify high risk National Pollution Discharge Elimination System (NPDES) facilities that don't submit Discharge Monitoring Reports (DMRs)

12_outputs: Risk scoring (Ranking from lowest to highest risk) for NPDES facilities that don't submit DMR's

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/31/2019
2_use_case_name: Clean Air Act (CAA) Predictive Analytics
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Enforcement and Compliance Assurance
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Risk scoring of Majors and Synthetic Minor Facilities to support CAA inspections

12_outputs: Decimal score between 0 and 1 for Major and Synthetic Minor CAA Facilities

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/31/2019
59_question_type: i
2_use_case_name: Extracting references from technical documents
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of the Administrator
8_topic_area: Energy & the Environment
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: In response to OMB interest—which we agree with—in giving researchers more visibility when we cite their work, National Center for Environmental Economics (NCEE) is investigating various methods to document citations in economic analysis and other regulatory documents. We have explored using GovChat to extract references from regulatory documents. 

12_outputs: Output would be lists of unique references by document, which can then be combined into a list with counts. The list could then be used to acknowledge researchers whose work is used by EPA. 
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Streamflow Duration Assessment Modeling
3_agency: Environmental Protection Agency
3_abr: EPA
4_bureau: Office of Water
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Streams exhibit a diverse range of hydrologic regimes, and the hydrologic regime strongly influences the physical, chemical, and biological characteristics of active stream channels and their adjacent riparian areas. One important aspect of the hydrologic regime is streamflow duration—the length of time that a stream sustains flowing surface water. Determining streamflow duration class can help support a variety of regulatory and non-regulatory water resource management decisions. This random forest modeling supports prediction of the appropriate streamflow duration classification across a range of geographies. Full information is available at: https://www.epa.gov/streamflow-duration-assessment/random-forest-modeling
12_outputs: Prediction of whether a streamflow is “perennial,” “intermittent,” or “ephemeral”
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/6/2009
19_date_acq_dev_began: 3/6/2009
20_date_implemented: 11/15/2011
27_public_info: No
28_iqa_compliance: Each SDAM was developed under an EPA developed and approved QAPP. 
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Pacific Northwest https://www.hydroshare.org/resource/fb4e7b8758d0478cbfe0d6c786f0f968/
Northeast and Southeast https://catalog.data.gov/dataset/nese-betasdam-final-data-and-code
Arid West https://catalog.data.gov/dataset/aw-betasdam-final-data-and-code
Western Mountains https://catalog.data.gov/dataset/wm-betasdam-final-data-and-code
Great Plains https://catalog.data.gov/dataset/gp-betasdam-final-data-and-code

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 – source code is publicly available.
39_code_link: https://github.com/USEPA/SDAM-web-app
40_has_ato: Yes
41_system_name: Data Management and Analytics Platform (DMAP) (Posit Connect)
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: 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.
2_use_case_name: Category Taxonomy Refinement Using NLP
3_agency: General Services Administration
3_abr: GSA
4_bureau: FAS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Uses token extraction from product descriptions more accurately shape intended markets for PSCs.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 11/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
25_hisp_name: Public Experience Portfolio – GSA 
26_public_service: Enable increased self-service through an expansion of digital costumer service tools
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link: It is available on the FAS Enterprise Data Architecture Github Repository
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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.
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
63_stakeholder_consult: None of the above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.
2_use_case_name: Key KPI Forecasts for GWCM
3_agency: General Services Administration
3_abr: GSA
4_bureau: FAS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Takes monthly historical data for underlying components used to calculate KPIs and creates near-term forecasts for the upcoming fiscal year. Pilot effort focuses on total agency/category spend (the denominator in multiple KPIs). If the pilot program is successful, the same methodology can be extended to other KPIs.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 11/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
25_hisp_name: Public Experience Portfolio – GSA 
26_public_service: Enable increased self-service through an expansion of digital costumer service tools
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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.
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
63_stakeholder_consult: None of the above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.
2_use_case_name: Enterprise Brain
3_agency: General Services Administration
3_abr: GSA
4_bureau: GSA IT (IDT)
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: Enterprise Brain is a document repository by Tanjo (tanjo.ai) that leverages AI to improve document discovery.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 10/1/2022
21_date_retired: 10/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QFCA21F0014
24_hisp_support: No
25_hisp_name: Public Experience Portfolio – GSA 
26_public_service: Enable increased self-service through an expansion of digital costumer service tools
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
39_code_link: Vendor is waiting to provide source code until the project is approved to go forward based on ROI.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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.
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
63_stakeholder_consult: None of the above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.
2_use_case_name: ServiceNow Virtual Agent (Curie)
3_agency: General Services Administration
3_abr: GSA
4_bureau: GSA IT (IDT)
8_topic_area: Other
8_question_type: ServiceNow Chatbot
10_commercial_ai: None of the above.
11_purpose_benefits: Virtual agent that uses ML to provide predictive results for chat entries. A natural language chatbot (virtual assistant), we named Curie, as part of a multi-model customer service experience for employee's IT service requests leveraging knowledge-based articles.
12_outputs: Text response generated the most likely answer base on the user input. 
16_dev_stage: Initiated
17_impact_type: Neither
22_dev_method: Developed with contracting resources.
24_hisp_support: No
25_hisp_name: Public Experience Portfolio – GSA 
26_public_service: Enable increased self-service through an expansion of digital costumer service tools
27_public_info: No
28_iqa_compliance: Non-public
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link: GSA GitHub
40_has_ato: Yes
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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.
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
63_stakeholder_consult: None of the above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.