GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Document Workflow / Intelligent Data Capture and Extraction
3_agency: General Services Administration
3_abr: GSA
4_bureau: GSA IT (IC)
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: GSA is driving towards a more accurate and scalable document workflow platform. GSA seeks to intelligently capture, classify, and transfer critical data from unstructured and structured documents, namely PDF files, to the right process, workflow, or decision engine.
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: No – agency does not have access to source code.
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: OAS Kudos Chatbot
3_agency: General Services Administration
3_abr: GSA
4_bureau: OAS / OCFO
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of Administrative Services would like to utilize a ChatBot to easily capture employee peer to peer recognitions. The OAS Kudos Chatbot leverages Natural Language Processing, inherent to Google Dialogflow, to facilitate a conversation between the AI software and the end user. This allows the chatbot to interpret and apply branching logic to what the user enters into the chat window.
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: Test Fit Layouts
3_agency: General Services Administration
3_abr: GSA
4_bureau: GSA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of the QBIQ pilot is to test the capabilities to rapidly test-fit spaces in consideration to evaluate whether AI can sufficiently be used to speed up vetting of space to fit requirements as well as utilize visualizations as a communication bridging tool with tenant agencies
12_outputs: Floor plan and 3D conceptual layouts
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: One-time micro-purchase for 3 test fits
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: Yes
31_data_catalog: No
33_agency_data: Floor plan and 3D conceptual layouts
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
41_system_name: qbiq.ai
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: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: Exposure of floorplans
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: Yes
2_use_case_name: Design Your Facility
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of Refugee Resettlement (ORR) manages many facilities for unaccompanied children and must regularly determine bed availability, configuration, and assignments. The current process for gathering this information takes lengthy training and assistance from system administrators.

The Design Your Facility interface leverages LLMs to help users more quickly configure the correct building, room, and bed layout needed to create their facility’s “digital twin” in the UC Bed Network Facility Mapper without lengthy training or deskside assistance from system administrators.
12_outputs: The Design Your Facility tool uses large language models (LLMs) to transform building layout information shared by users in a conversational manner into data about buildings, rooms, and bed layers within the Unaccompanied Children (UC) Bed Network tooling.

The AI model suggests object configurations that should be applied to correctly model a facility’s “digital twin” of the building, room, and bed layout. The workflow relies solely on user descriptions and user confirmations.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 4/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75D30122F15723
31_data_catalog: No
33_agency_data: No training or fine-tuning; we are using secure commercially available LLMs.
34_data_docs: Documentation has been partially completed
40_has_ato: Yes
41_system_name: Horizon (ACF instance of Palantir Foundry)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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.
2_use_case_name: Discover Financial Business Intelligence System (FBIS) Report Analysis (Sub-CAN Line Items)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This project provides a resource for ACF Discover users in the budget community to identify related line items across disparate reports from FBIS. The process searches the data and aggregates information (e.g. supplier name, document number, total obligations) to create an up-to-date, real-time spend plan that pulls from the open-closed report, requisition purchase order report, and integrates with funds distribution report.

AI returns line items from the open-closed report and requisition purchase order report related to the sub-CAN line item description such as supplier name, document number, document type, and total obligations for that sub-CAN line item. The Discover FBIS Report Analysis (Sub-CAN Line Items) LLM helps find related obligations and line items from various reports and returns information crucial for a budget officer when they are creating a spend plan for their office. The LLM aids ACF Discover track various line items and pulls the information into one place: the Spend Plan module where ACF can track yearly budgets and monitor budget health.
12_outputs: A large-language model helps compile an aggregated report of costs by intaking a specific Congressional Appropriation Number (CAN), category, sub-CAN line item descriptions (which are not standardized), and a projection cost from the user (either by a CSV or manual entry).
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75P00122A00010/75P00122F37003/P00004
31_data_catalog: No
33_agency_data: No training or fine-tuning. HHS-FBIS reports are used as a data source for retrieval-augmented generation.
34_data_docs: Documentation is complete
40_has_ato: Yes
41_system_name: ACF Discover (HHS instance of Palantir Foundry)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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.
2_use_case_name: Grant Spend Health Analysis
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The mission of the Office of Grants Management (OGM)is to provide high-quality fiscal stewardship and management for ACF-funded recipients across the country and in U.S. territories. This includes responsive oversight and technical assistance for discretionary awards, non-discretionary awards, and cooperative agreements. OGM seeks ways to prioritize proactive technical assistance to grant recipients based on likelihood that awarded funds may not be fully utilized. 

The goal is to identify still-active grants that are potentially on the path towards deobligating funding. This information would be visible to grant specialists within ACF. OGM anticipates developing customized training and technical assistance materials that can assist recipients if we predict that their grant is trending towards leaving funds unutilized.
12_outputs: As part of this project, we are conducting exploratory analyses including supervised and unsupervised AI techniques to determine whether the data we have on grant spend over time can power a predictive model that reasonably identifies grants with a higher likelihood of under-utilizing funds. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/1/2024
19_date_acq_dev_began: 3/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75ACF123F80045
31_data_catalog: Yes
33_agency_data: Grant financial data from usaspending.gov and the Payment Management System. Recipient data from SAM.gov.
34_data_docs: Documentation is widely available
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Structuring and Validating Completeness of Case Data Information
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: In accordance with the Ms L vs ICE settlement, the U.S. Customs and Border Protection (CBP) must provide specific pieces of information about family separations. Beginning November 2024, CPB will release this information. ACF's Office of Refugee Resettlement (ORR) plans to add family separation information to existing child profile information for reference.

The AI output structures free form data into a more easily searchable format. These data will be used to assist in the placement process.
12_outputs: Creates a structured data asset of  the critical data points needed about a separation case. AI will be used to conduct initial parsing of the data provided by CBP and highlight whether or not the required fields from the Ms L vs. ICE settlement are included and can therefore be updated into the child's profile in ORR's data system.  ORR's Intakes Team does final review and in cases where data appears to be missing, the ORR Intakes Team reaches back out to CBP for that information.
16_dev_stage: Acquisition and/or Development
17_impact_type: Safety-Impacting
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 11/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75D30122F15723
31_data_catalog: No
33_agency_data: No training or fine tuning; we are prompting secure commercially-available LLMs.
34_data_docs: Documentation has been partially completed
40_has_ato: Yes
41_system_name: Horizon (ACF instance of Palantir Foundry)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Triaging Notice of Concern Submissions
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of Refugee Resettlement (ORR) has amassed a large backlog of Notice of Concern (NOC) PDF forms that contain critical information regarding safety of children who have left ORR's care. 

The AI-powered triage tool will support Office of Refugee Resettlement staff more effectively and efficiently review NOCs by helping structure and prioritize received NOCs. Through more effective triage, ORR will begin to cut down on the large backlog that has accumulated.
12_outputs: In the NOC triage system, AI will be used for two purposes:

1. We will use AI to parse the NOCs to generate structured fields from the text and validate categorizations.

2. AI will provide prioritization recommendations -- but not make decisions -- on the most critical NOCs for the ORR team to review first. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Safety-Impacting
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 6/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75D30122F15723
31_data_catalog: No
33_agency_data: No training or fine tuning.  Notional NOCs were used to prompt engineer secure commercially-available LLMs and evaluate performance. End-user testing was used to evaluate on real NOCs.
34_data_docs: Documentation has been partially completed
40_has_ato: Yes
41_system_name: Horizon (ACF instance of Palantir Foundry)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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.
2_use_case_name: Unaccompanied Children Program Policy & Procedure Research Tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ACF
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of Refugee Resettlement (ORR) conducts monitoring visits at least monthly to ensure that care providers meet minimum standards for the care and timely release of unaccompanied children, and that they abide by all Federal and State laws and regulations, licensing and accreditation standards, ORR policies and procedures, and child welfare standards. If ORR monitoring finds a care provider to be out of compliance with requirements, ORR issues corrective action findings and requires the care provider to resolve the issue within a specified time frame. Compliance determination involves research into the various laws, standards, policies, and procedures.

The UC Program Policy & Research Tool assists the UC monitoring team with research that in many cases take a long time. The goal of the  UC Program Policy & Research Tool is to speed up this research as children's health and well-being may be impacted before a corrective action finding is issued and the issue is resolved.
12_outputs: The UC Program Policy & Procedure Research Tool speeds up research of relevant laws, standards, policies, and procedures content curated and approved by ORR's policy team. This research is one part of the process that informs ORR's monitoring team's decisions on whether corrective actions are needed and if so, what corrective actions. AI is not used to suggest corrective actions but rather support determination of whether care providers are in compliance.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 11/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75D30122F15723
31_data_catalog: Yes
33_agency_data: No training or fine-tuning. ORR-approved policy content are used as a data source for retrieval-augmented generation.
34_data_docs: Documentation is complete
40_has_ato: Yes
41_system_name: Horizon (ACF instance of Palantir Foundry)
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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.