GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Enhance Market Surveillance and Fraud Detection Use Case 
3_agency: Federal Energy Regulatory Commission
3_abr: FERC
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The AI solution aims to assist staff in analyzing large and complex market datasets for market integrity and compliance purposes. It will process and interpret vast amounts of trading data at high speed, identifying anomalies, trends, and potential regulatory violations. This capability will help maintain fair and transparent markets, enabling staff to focus on strategic decision-making. The AI’s efficiency and precision will save time, improve compliance, and uphold public trust in market operations.
12_outputs: Initial Analysis of market data
17_impact_type: Neither
23_contract_piids: 89603024F0006
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: We have not started work on this use case and have not identified specific data sets needed to support all market surveillance activities. 
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.
35_demo_features: Other
35_question_type: No demographic variables will be used.
37_custom_code: Yes
40_has_ato: No
43_infra_provisioned: Other
43_question_type: This use case has not been initiated at the time of this reporting. Once initiated, the answer will be yes to the IT infrastructure provisioning.
45_compute_request: Yes
47_timely_resources: Other
47_question_type: This use case has not been initiated at the time of this reporting. Once initiated, the answer will be yes to the IT infrastructure provisioning.
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: 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.
2_use_case_name: Support Interconnection Request Responses Use Case 
3_agency: Federal Energy Regulatory Commission
3_abr: FERC
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: The AI solution will be designed to address the growing backlog of project interconnection requests and reduce the average three-year wait time. By accelerating the processing of proposals, the AI will help FERC with cost savings and shorting the timelines of requests. 
12_outputs: Request analysis of interconnection requests
17_impact_type: Neither
23_contract_piids: 89603024F0006
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: We have not started work on this use case and have not identified specific data sets needed to support all interconnection requests. 
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.
35_demo_features: Other
35_question_type: No demographic variables will be used.
37_custom_code: Yes
40_has_ato: No
43_infra_provisioned: Other
43_question_type: This use case has not been initiated at the time of this reporting. Once initiated, the answer will be yes to the IT infrastructure provisioning.
45_compute_request: Yes
47_timely_resources: Other
47_question_type: This use case has not been initiated at the time of this reporting. Once initiated, the answer will be yes to the IT infrastructure provisioning.
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: 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.
2_use_case_name: LexisNexis
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OGC
8_topic_area: Mission-Enabling
10_commercial_ai: Generative AI-assisted legal research tool
11_purpose_benefits: Generative AI-assisted legal research tool
12_outputs: LexisNexis uses generative AI to search for and summarize legal information, resulting in more efficient legal work for FHFA.
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Neural Networks for FHFA Modeling Analytics Platform (FMAP)
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: DHMG
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Help researchers optimize the framework and specifications of the production of the Single-Family FHFA Modeling Analytics Platform (FMAP).
12_outputs: This tool uses neural networks to identify nonlinearities, anomalies, and important variables in loan-level mortgage data, which helps FHFA staff improve forecasts from the Single-Family Modeling Analytics Platform.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/8/2023
19_date_acq_dev_began: 1/8/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
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.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
41_system_name: Machine Learning for SF FMAP specification  
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: No
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: Phishing Email Identification
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCOO
8_topic_area: Mission-Enabling
10_commercial_ai: Prioritizing and categorizing incoming emails using AI.
11_purpose_benefits: Knowbe4 PhishER is a tool to report and manage suspicious emails. This tool use ML and AI to analyze suspicious emails to identify if the email is spam, phishing, or malicious. The primary benefit of this tool is to automate responses without requiring human intervention to analyze each email that end users identify as suspicious.
12_outputs: Output is a response to suspicious emails without requiring a cybersecurity specialist to analyze hundreds of emails. This tool reduces time to respond to end user requests concerning suspicious activity.  
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: WestLaw
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OGC
8_topic_area: Mission-Enabling
10_commercial_ai: Generative AI-assisted legal research tool
11_purpose_benefits: Generative AI-assisted legal research tool
12_outputs: WestLaw uses generative AI to search for and summarize legal information, resulting in more efficient legal work for FHFA.
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Public Comments Summarization and Topic Classifications Pilot Project
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: DBR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: To summarize and classify comments received by FHFA during public comment processes.
12_outputs: This tool uses natural language processing to rapidly summarize and classify certain comments received from the public, increasing FHFA staff efficiency.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
26_public_service: Request for Comments analysis
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Data consist solely of public comments received by FHFA.
33_agency_data: Publiccomments
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.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
41_system_name: Public Comments AnlysisTool Pilot Project (PCAT
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: No
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.
2_use_case_name: Virtual Acquisition Office (VAO) Ally 
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OBFM
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: VAO Ally is a built-in, useable feature as part of the overall VAO subscription. VAO Ally can reduce the amount of time necessary to research acquisition-related policy/guidance questions and scenarios by parsing through multiple information resources and providing responses/answers based on a user’s question searched.
12_outputs: VAO Ally provides a summarized response/answer to the user’s question within the overall web-based VAO subscription. Additionally, VAO Ally provides the source materials (e.g., VAO content, regulatory resources such as the FAR, etc.) that supported its answer/output.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Analytics,, Search, Queries and LLM integration for data management on Oracle
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Oracle Enterprise 23AI has built in AI for predictive analytics, AI Vector search, Natural Language queries and integration with Large Language Models
12_outputs: Oracle Enterprise 23AI has built in AI for predictive analytics, AI Vector search, Natural Language queries and integration with Large Language Models
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Python modules to support AI/ML
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: Multiple Python modules are installed to support AI and ML
12_outputs: Multiple Python modules are installed to support AI and ML
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: R modules to support AI/ML
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: Multiple R modules are installed to support AI and ML
12_outputs: Multiple R modules are installed to support AI and ML
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Security and network monitoring using Cisco Identify Services engine
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Improves security visibility, segmentation, and threat response by analyzing network traffic and user behavior in real-time, enabling automatic detection, isolation, and resolution of potential security risks.
12_outputs: Cisco Identity Services Engine (ISE) uses AI for network visibility, segmentation, and threat response by analyzing network traffic and user behavior.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Desktop productivity using Microsoft 365
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Collaborating in real-time using AI-assisted tools in word processors.
11_purpose_benefits: To help foster smart recommendations, data insights, and enhanced collaboration.
12_outputs: Microsoft 365 suite (Teams, SharePoint, Excel, PowerPoint, Word) uses AI for various functionalities like language translation, smart recommendations, data insights, and enhanced collaboration.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Virtual Desktop using Citrix 
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Citrix uses AI to optimize virtual desktops, manage workloads, and enhance security with behavioral analysis and threat detection.
12_outputs: Citrix uses AI for optimizing virtual desktop performance, managing workloads, and enhancing security through behavioral analysis and threat detection.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Analytics, Indexing, and Anomaly Detection for Data Management on SQL Server 
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Predictive analytics, automated indexing, and anomaly detection in data management and queries.
12_outputs: SQL Server incorporates AI for predictive analytics, automated indexing, and anomaly detection in data management and queries.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Workflow automation and predictive analytics using ServiceNow
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Logging and analyzing time spent on tasks using AI-powered time management tools.
11_purpose_benefits: Help to automate workflows, predictive analytics, and providing intelligent service management solutions.
12_outputs: ServiceNow utilizes AI for automating workflows, predictive analytics, and providing intelligent service management solutions.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Security and Compliance in file sharing and collaboration using Kiteworks
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Enhances security and compliance in file sharing and collaboration by detecting anomalies and potential threats in real-time."
12_outputs: Kiteworks employs AI to enhance security and compliance in file sharing and collaboration by detecting anomalies and potential threats.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Network monitoring, anomaly detection through Whats Up Gold and Flowmon integration
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Uses AI for network visibility, anomaly detection, and security through Flowmon integration
12_outputs: Uses AI for network visibility, anomaly detection, and security through Flowmon integration.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Mathematical formula transcription using Commonlook Online
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Commonlook Online is used to translate complex mathematical formula into plain language for publicly released documents.
12_outputs: Commonlook Online is used to translate complex mathematical formula into plain language for publicly released documents.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Mobile Worker Productivity using Apple iOS platform
3_agency: Federal Housing Finance Agency
3_abr: FHFA
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: The Apple iOS platform provides multiple applications with Natural Language Processing (NLP) capability.  This is a predictive word  technology where the platfom makes an assumption in real time of the word that user is going to type, and allows for a single tap selection.
12_outputs: The Apple iOS platform provides multiple applications with Natural Language Processing (NLP) capability.  This is a predictive word technology where the platform makes an assumption in real time of the word that user is going to type and allows for a single tap selection.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Economic Trend Modeling
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Monetary Affairs
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Additional data beyond traditional statistical models
12_outputs: Multiple methods calculate the probability of a economic event in the next twelve months between 0 and 1 and are aggregated
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2019
19_date_acq_dev_began: 3/1/2019
20_date_implemented: 9/23/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Internal- Fixed Income
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.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: Document Organization Tool
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Reserve Bank Operations and Payment Systems
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: To enhance the efficiency and organization of reference materials to support adversarial analysis
12_outputs: Labeled documents which improve searchability
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: PDF Optical Character Recognition (Text)
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Information Technology
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: The intended purpose is to extract and to decide the correct column name extracted from a report in PDF format
12_outputs: The list of the appropriate column names in text format
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/19/2021
19_date_acq_dev_began: 9/22/2021
20_date_implemented: 9/26/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: 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.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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.
2_use_case_name: PDF Optical Character Recognition (Images)
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Information Technology
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: The intended purpose is to extract text from the image embedded in PDF file
12_outputs: The AI system's output is the image details in text format for further analysis
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/11/2024
19_date_acq_dev_began: 7/17/2024
22_dev_method: Developed in-house.
24_hisp_support: No
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.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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.