GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Counterparty Risk Anomaly Detection 
3_agency: Department of Housing and Urban Development
3_abr: HUD
4_bureau: Ginnie Mae 
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Ginnie Mae is responsible for analyzing counterparty risk profiles of mortgage issuers who participate in Ginnie Mae’s program. Ginnie Mae analyzes data from multiple sources to identify potential risks and areas of focus. To enhance the identification of data patterns, Ginnie Mae uses machine learning algorithms, specifically clustering and genetic techniques. These algorithms detect potential risk areas, enabling a focused approach to subsequent analysis by Ginnie Mae staff. 
12_outputs: Data patterns 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2020
19_date_acq_dev_began: 8/1/2020
20_date_implemented: 5/1/2022
21_date_retired: N/A 
22_dev_method: Developed with contracting resources.
23_contract_piids: GSQ1117BJ0043 
24_hisp_support: No 
27_public_info: No 
28_iqa_compliance: Ginnie Mae has established Data Certification policies with standards for accuracy, completeness, and reasonableness.  Ginnie Mae also follows HUD’s Project Planning & Management discipline that establishes requirements for data quality and management. 
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: This is not a self-learning system.  Although the data is not used to train, fine-tune, and/or evaluate performance, the following sources are used by the ML model to perform analysis: 
- GNMA Investor Reported MBS portfolio data aggregated on an issuer level 
- MBFRF (Mortgage Banking Financial Reporting Form) Data for counterparty financial information 
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: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Ginnie Mae Reporting and Feedback System (RFS) 
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 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.
51_extension_request: No - Use Case is not Rights or Safety impacting, section 5 does not apply
2_use_case_name: Subledger Data Quality Machine Learning
3_agency: Department of Housing and Urban Development
3_abr: HUD
4_bureau: Ginnie Mae 
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Ginnie Mae analyzes Master Sub-Servicer (MSS) transaction data on a monthly cadence. Through its use of machine learning models, Ginnie Mae has enhanced its ability to identify data inconsistencies and exceptions associated with its MSS transaction data. By automating the analysis of a significant number of MSS transactions, the machine learning algorithms enhance the efficiency and accuracy of key reporting processes. 
12_outputs: The AI solution identifies data anomalies. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/20/2020
19_date_acq_dev_began: 11/20/2020
20_date_implemented: 4/21/2021
21_date_retired: N/A 
22_dev_method: Developed with contracting resources.
23_contract_piids: GS11Q16BJC0005 
24_hisp_support: No 
27_public_info: No 
28_iqa_compliance: Ginnie Mae has established Data Certification policies with standards for accuracy, completeness, and reasonableness.  Ginnie Mae also follows HUD’s Project Planning & Management discipline that establishes requirements for data quality and management. 
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: This is not a self-learning or self-refining system.  Although the data is not used to train, fine-tune, and/or evaluate performance, the following sources are used:  
- Transaction data from Ginnie Mae Master Sub-Servicers for the defaulted Single-Family non-pooled assets (this data does not include any PII). 
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: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Ginnie Mae Financial Accounting System  
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 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.
51_extension_request: No - Use Case is not Rights or Safety impacting, section 5 does not apply
2_use_case_name: Automating Draft Counterparty Credit Narrative Reports
3_agency: Department of Housing and Urban Development
3_abr: HUD
4_bureau: Ginnie Mae 
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Ginnie Mae performs counterparty credit reviews of mortgage issuers who participate in Ginnie Mae’s program. The reviews are based on written analysis and financial data. As an initial step in counterparty credit reviews, Ginnie Mae uses Natural Language Generation (NLG) to implement coded rules and generate draft narratives. This application of AI enables processing efficiency and reduces errors that can occur in a manual process. 
12_outputs: Draft memos/reports 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/21/2021
19_date_acq_dev_began: 3/1/2021
20_date_implemented: 4/1/2022
21_date_retired: N/A 
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QFDA19A0008 
24_hisp_support: No 
27_public_info: No 
28_iqa_compliance: Ginnie Mae has established Data Certification policies with standards for accuracy, completeness, and reasonableness.  Ginnie Mae also follows HUD’s Project Planning & Management discipline that establishes requirements for data quality and management. 
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: This is not a self-learning or self-refining system.  The reports are generated from business rules and format established by Ginnie Mae staff.  Although the data is not used to train, fine-tune, and/or evaluate performance, the following sources are used:  
-GNMA Investor Reported MBS portfolio data aggregated on an issuer level 
- MBFRF (Mortgage Banking Financial Reporting Form) Data for counterparty financial information 
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: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Ginnie Mae Reporting and Feedback System (RFS) 
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: 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.
51_extension_request: No - Use Case is not Rights or Safety impacting, section 5 does not apply
2_use_case_name: Quantitative Text Analysis
3_agency: Department of Housing and Urban Development
3_abr: HUD
4_bureau: Office of the Chief Financial Officer (OCFO)
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Analysis of large amounts of text for patterns (commonly appearing words, common word clusters, etc.)
12_outputs: The outputs are charts and graphs showing which words appear most commonly across the documents, which words appear clustered together most often, and the sentiment of the words, etc. 
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 11/1/2023
20_date_implemented: 12/1/2023
21_date_retired: 1/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: GS-00F-275CA-86615121A00004, TO 86615122F0020
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
37_custom_code: No
41_system_name: Leveraged Audient Group contract service to produce output
51_extension_request: No - Use Case is not Rights or Safety impacting, section 5 does not apply
2_use_case_name: NextGen Advanced Methods: Air Traffic Control System Command Center (ATCSCC) Webinar Speech2Text and Analysis
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Ames Research Center
8_topic_area: Natural Language Processing
10_commercial_ai: None of the above.
11_purpose_benefits: The Advanced Methods project explores the use of innovative and emerging technologies to drive post operational analysis of Traffic Management for aircraft. Technologies such as machine learning, (ML), artificial intelligence (AI), and advanced data analytics for use in improving the FAA's traffic flow management. In this specific use case, our aim is to use deep learning to convert live ATCSCC webinar meeting conversation to text, and then apply natural language processing to the converted text data for later analysis and review.
12_outputs: Text from speech to text and NLP of air traffic management content.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Operated in an approved enclave
41_system_name: ATCSCC
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
49_existing_reuse: This project leveraged external NLP work
50_internal_review: Yes
2_use_case_name: NextGen Data Analytics: Letters of Agreement
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Ames Research Center
8_topic_area: Natural Language Processing
10_commercial_ai: None of the above.
11_purpose_benefits: Today, operation constraints are documented via Standard Operating Procedures (SOP) and Letters of Agreement (LOA) and are not made available to the public in a consistent manner. SOPs are specific to an air traffic control facility and specify the procedures necessary for safe operation in the sector. LOA outline agreements establishing procedures and responsibilities between two parties (including crossing restrictions, holding patters, emergency procedure coordination, etc.) The LOA/SOPs are published internally as scanned PDFs and are the responsibility of the facility to maintain. To reduce the manual effort of tagging the documents for ease of reference, there is an opportunity to use modern data analytics and machine learning to produce and disseminate constraints in a standardized manner. 
Providing LOAs or SOPs to stakeholders will enable flight planners (pilots and vendors) to study or ingest this information and thereby plan flight trajectories that remain consistent with air traffic constraints. It is also fundamental to Next Gen capabilities to share accurate data for purposes of creating new noise abatement procedures; improve NAS information for common situational awareness and alignment to implement new tools to assist in future time-based flow management.
12_outputs: Test-based digitized SOPs and LOAs.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Letter of agreement dataset from FAA
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Operated in an approved enclave
41_system_name: Checking
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
49_existing_reuse: This project used external text mining capabilities
50_internal_review: Yes
2_use_case_name: AEGIS: Autonomous Exploration for Gathering Increased Science
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Deep Learning
10_commercial_ai: None of the above.
11_purpose_benefits: AEGIS enables intelligent targeting and data acquisition by planetary rovers. It uses computer vision techniques to identify targets (e.g., rocks) in wide angles images of the rover's surrounding terrain. If targets are found that match scientists specificiations, they are then measured autonomously using remote sensing instruments. AEGIS was first used on the MER Mission. It is currently in use on the MSL Mission to acquire data for the ChemCam instrument. It is planned for use in Spring of 2022 on the M2020 Mission to acquire data for the SuperCam instrument.
12_outputs: Recommendations of relevant objects, e.g., Mars rocks, for scientific examination.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2006
19_date_acq_dev_began: 1/1/2007
20_date_implemented: 1/1/2010
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_question_type: Blank/Not Provided
33_agency_data: public Mars science data related to sensors for optimizing scientific measurements
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Mars2020 Rover
49_existing_reuse: Mars 2020 team built on prior rovers and shared across different facets of Mars 2020.
50_internal_review: Yes.  Peer review
2_use_case_name: ASPEN Mission Planner
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Statistical Methods
10_commercial_ai: None of the above.
11_purpose_benefits: Based on AI techniques, ASPEN is a modular, reconfigurable application framework which is capable of supporting a wide variety of planning and scheduling applications. ASPEN provides a set of reusable software components that implement the elements commonly found in complex planning/scheduling systems, including: an expressive modeling language, a resource management system, a temporal reasoning system, and a graphical interface.  ASPEN has been used for many space missions including: Modified Antarctic Mapping Mission, Orbital Express, Earth Observing One, and ESA's Rosetta Orbitter.
12_outputs: Plan and schedule recommendations to optimize Science from Scientific Sensors
16_dev_stage: In mission
17_impact_type: Neither
18_date_initiated: 1/1/1997
19_date_acq_dev_began: 1/1/1998
20_date_implemented: 1/1/2002
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Data related to optimizing sensors and flight hardware.  Note This is a mission-embedded software component subject to system engineering and mission planning reviews, not the IT ATO process.  PII, SAOP, HISP, etc. do not apply.
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Multiple approved space-based systems
43_infra_provisioned: No
49_existing_reuse: This capability has been re-used among many missions
50_internal_review: Yes.  Peer review
2_use_case_name: CLASP Coverage Planning & Scheduling
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Statistical Methods
10_commercial_ai: None of the above.
11_purpose_benefits: The Compressed Large-scale Activity Scheduling and Planning (CLASP) project is a long-range scheduler for space-based or aerial instruments that can be modeled as pushbrooms 1D line sensors dragged across the surface of the body being observed. It addresses the problem of choosing the orientation and on/off times of a pushbroom instrument or collection of pushbroom instruments such that the schedule covers as many target points as possible, but without oversubscribing memory and energy. Orientation and time of observation is derived from geometric computations that CLASP performs using the SPICE ephemeris toolkit.  CLASP allows mission planning teams to start with a baseline mission concept and simulate the mission's science return using models of science observations, spacecraft operations, downlink, and spacecraft trajectory. This analysis can then be folded back into many aspects of mission design -- including trajectory, spacecraft design, operations concept, and downlink concept. The long planning horizons allow this analysis to span an entire mission.  Actively in use for optimized scheduling for the NISAR Mission, ECOSTRESS mission (study of water needs for plant areas), EMIT mission (mineralogy of arid dusty regions), OCO-3 (atmospheric CO2) and more as well as used for numerous missions analysis and studies (e.g. 100+).
12_outputs: Estimates of scientific mission outcomes / results, based on optimized scheduling of spacecraft and sensors.
16_dev_stage: In mission
17_impact_type: Neither
18_date_initiated: 1/1/2006
19_date_acq_dev_began: 1/1/2006
20_date_implemented: 1/1/2018
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Mission planning and execution data from multiple missions.
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Deployed on multiple remote sensing spacecraft
43_infra_provisioned: No
49_existing_reuse: This capability has been re-used among many missions
50_internal_review: Yes
2_use_case_name: Enhanced AutoNav for Perseverance Rover on Mars
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Classification
10_commercial_ai: None of the above.
11_purpose_benefits: AutoNav on the Perseverance Rover autonomously plans a safe path based on stereo navigation camera images, based on multiple technologies including a tree search for decision making, Dijkstra algorithm for global path planning, stereo processing for 3D terrain reconstruction, and Approximate Clearance Evaluation (ACE) for safety checks. It is deployed on Perserverance rover and being used for autonomous driving on Mars.
12_outputs: Recommended navigation path for Mars Rover.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2015
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 7/1/2020
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Sensor and imagery data from prior Mars missions and from terrestrial-based physical simulation sites.
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Mars2020 Rover
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
49_existing_reuse: Reused selected techniques from prior Mars rovers
50_internal_review: Yes
2_use_case_name: Mars2020 Rover (Perseverance)
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: Research, experiments, and engineering to empower future rovers with onboard autonomy; planning, scheduling & execution; path planning; onboard science; image processing; terrain classification; fault diagnosis; and location estimation.  This is a multi-faceted effort and includes experimentation and demonstrations on-site at JPL's simulated mars navigation yard.  The M2020 onboard scheduler incrementally constructs a feasible schedule by iterating through activities in priority-first order. When considering each activity it computes the valid time intervals for placement, taking into account preheating, maintenance heating, and wake/sleep of the rover as required. After an activity is placed (other than a preheat/maintenance or wake/sleep), the activity is never reconsidered by the scheduler for deletion or moving. Therefore the scheduler can be considered non backtracking, and only searches in the sense that it computes valid timeline intervals for legal activity placement.  Meta Search: Because the onboard scheduler will be invoked many times in a given sol (Martian Day) with a range of possible contexts (due to execution variations), its non backtracking nature leaves its vulnerable to brittleness. In order to mitigate this potential brittleness, the Copilot systems perform a monte carlo based stochastic analysis to set meta parameters of the scheduler - primarily activity priority but also potentially preferred time and temporal constraints.
12_outputs: Mission-priority-based recommendations for scheduling Mars2020 Rover activities.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2015
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 7/1/2020
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Multiple forms of rover mission data
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Mars2020 Rover
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: no
49_existing_reuse: Reused selected techniques from prior Mars rovers
50_internal_review: Yes