GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Automated Detection of Personally Identifiable Information (PII) in Cybersecurity Data
Agency: Department of Homeland Security - CISA
Problem addressed: To enhance privacy, this AI tool uses Natural Language Processing (NLP) to automatically flag potential PII for review and removal by CISA analysts.
Purpose and benefits: Automated PII Detection and Review Process uses analytics to identify and manage potential PII in submissions. If PII is flagged, the submission is sent to CISA analysts, who are guided by AI to review and confirm or reject the detection, redacting information if necessary. Privacy experts monitor the system and provide feedback. The system learns from this feedback, ensuring compliance with privacy regulations and improving efficiency by reducing false positives. Regular audits ensure the process remains trustworthy and effective.
Topic area: Admin Functions
Development stage: Deployed
First used: 2020-12-01
US federal AI use case: Vault Access Log (SPVAA)
Agency: Department of Homeland Security - CBP
Problem addressed: Automate identification of personnel entering into secure seized property vault.
Purpose and benefits: The system will enhance monitoring and minimizes the risk of unauthorized access, contributing to stronger security protocols for handling seized property.
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case uses facial recognition technology in Seized Property Vault Activity Automation (SPVAA) to create a log of access to a seized property vault. Photos of the CBP personnel accessing the vault are loaded into the application and logs the entrance request, the case number associated with the entrance request, and the individual’s access to the vault.
First used: 2022-08-01
US federal AI use case: Report Analysis and Archive System (RAAS)
Agency: Department of Homeland Security - CWMD
Development stage: Retired
US federal AI use case: Biometric Check-in for ATD-ISAP (SmartLINK)
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the need for frequent in-person check-ins for participants in the ATD-ISAP program.
Purpose and benefits: ISAP Biometric Monitoring App is a technology option that allows participants to report in using a smartphone. This app verifies a participant’s identity, determine their location, and quickly collect status change information. The app adds functionality not available with telephonic and is less intrusive than a GPS unit. ISAP monitoring app limits in-person interactions of routine check-ins, allowing more time to be allocated to non-compliant participants, complex removal proceedings cases and docket management.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Facial verification is only one option for check-in. If the remote check-in fails, either because it was unable to verify the match between the user and their previously taken photo, or because of other potential issues (poor lighting, camera/phone malfunction, etc...), an officer will manually review the check-in photo against the previously taken photos. If that fails, the user can schedule an in-person check-in at their local ERO office. Therefore, the output of AI (facial verification for a remote check-in) is not the primary basis for a decision or action that would affect the individual's rights or safety. It is a convenience to help save time for both the user and officers.
First used: 2018-02-01
US federal AI use case: I-765 - USCIS Facial Recognition through IDENT (1:1 Face Recognition/Validation)
Agency: Department of Homeland Security - USCIS
Problem addressed: Using the Automated Biometric Identification System (IDENT) makes this process nearly instant and greatly enhances processing efficiency without compromising the effectiveness of current identity verification methods. Additionally, new rules created by the FBI’s Compact Council require biometric verification to perform fingerprint resubmissions for different filing reasons than the original fingerprint capture. Facial verification brings USCIS into compliance with this rule change. Furthermore, performing facial verification increases the integrity of information and identities by identifying conflicts early on, preventing issues from becoming pervasive across immigration systems.
Purpose and benefits: This will allow the user to complete the biometric verification requirement without having to attend an appointment at am Applicant Support Center. This reduces the burden on the beneficiary as well as reducing demands on USCIS Applicant Service Center resources.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-11-12
US federal AI use case: I-765 - USCIS Face Capture Mobile App
Agency: Department of Homeland Security - USCIS
Development stage: Retired
Risk category: High-impact
US federal AI use case: Criminal Investigations (OBIM)
Agency: Department of Homeland Security - USSS
Problem addressed: The intended problem to solve is the identification of unknown victims and suspects involved in crimes that undermine the integrity of U.S. financial and payment systems. By using facial recognition and biometric image comparison, the USSS aims to efficiently and accurately identify individuals connected to criminal investigations, thereby supporting law enforcement efforts to detect, arrest, and prevent such crimes.
Purpose and benefits: The intended purpose of this AI is so that USSS personnel may submit available photographs or video stills of these unknown persons as probe images (facial images or templates searched against the gallery of an FRS) to other government agencies for comparison against their image galleries. The agencies will query their image galleries of known persons and may provide lists of potential matches. They may use the potential matches to produce investigative leads which will assist in the further identification of victims or suspects. Additionally, we may request another government agency to conduct a one-to-one comparison of two photographs or video stills for investigative use.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2017-01-01
US federal AI use case: Machine Learning Analysis Applied to Cyber Threat Hunt Data
Agency: Department of Homeland Security - TSA
Problem addressed: The Use Case addresses the problem of how to maximally assess available data to identify anomalies and other patterns that may inform the cyber threat hunt process.
Purpose and benefits: Cyber threat hunts typically involve a vast amount of data. Machine learning models can quickly and efficiently process this data as well as more effectively identify anomalous activity than humans. 
This could improve the efficiency and quality of cyber threat hunts by detecting suspicious behavior more quickly and increasing the amount of data that can be analyzed during a hunt.
Topic area: Cybersecurity
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI outputs do not produce an action or serve as a principal basis for a decision that has the potential to significantly impact the safety of human-life or well-being, climate or environment, critical infrastructure, or strategic assets or resources.

This use case detects anomalies or outliers, and also may be used to classify or categorize certain activity within data that has already been collected during an authorized cyber threat hunt operation within TSA’s networks. The AI outputs are reviewed by a human analyst team to determine if any of the patterns might be associated with unusual activity. The analyst would then continue to investigate further as during normal threat hunt operations.
US federal AI use case: Spending Analysis and Budget Execution Risk (SABER) Model
Agency: Department of Homeland Security - MGMT
Problem addressed: Identify account at risk for over- and under-spend.
Purpose and benefits: Purpose: Predicts potential budget execution issues by analyzing historical spending patterns across various Treasury accounts and classifications.
Benefits: Early identification of spending anomalies allows proactive budget management and reduces the risk of under/overspending.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AdaptiveMFA
Agency: Department of Homeland Security - MGMT
Problem addressed: Enhances security and the user experience, including: Behavioral Anomaly Detection - AI monitors user activity over time to establish a baseline pattern of their behavior.  If a login attempt deviates significantly, such as logging in from a foreign country, unknown workstation, or disallowed IP address space, Okta can trigger Adaptive Multi-factor Authentication (aMFA) to block access. Adaptive MFA - Okta's AI-powered Adaptive MFA tailors security challenges based on risk level. Trusted users might only need a password, while high-risk users are prompted for biometric or application-based verification. Real-Time Threat Detection - Okta's integration with AI-driven threat intelligence platforms like CrowdStrike and Microsoft Defender enhance real-time visibility into threats, correlating data from endpoint, network, and identity layers. Access Governance with Intelligence - AI enables smarter access reviews and role recommendations.  It detects unusual access rights, flags overprovisioned users, and automatically suggests changes. AI is integrated into DHS's identity and access management solutions to strengthen security and enhance the user experience.
Purpose and benefits: Adaptive Multi-factor Authentication (aMFA) introduces additional intelligence into Identity flows by taking into account the authentication context data during the authentication. Using the data, DHS is able to adapt security and authentication policies to enhance the security to DHS systems.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-03-30
US federal AI use case: Text Analytics for Survey Responses (TASR)
Agency: Department of Homeland Security - MGMT
Problem addressed: Quickly and accurately pulling significant topics and themes from unstructured text responses to DHS internal surveys.
Purpose and benefits: The intended purpose of the AI is to perform topic modeling,  sentiment analysis, or other text classification tasks on responses provided to internal staff DHS Pulse Survey questions.

Text Analytics for Survey Responses (TASR) is an application for performing Natural Language Processing (NLP) and text analytics on survey responses. It is currently being applied by DHS Office of the Chief Human Capital Officer (OCHCO) to analyze and extract significant topics/themes from unstructured text responses to open-ended questions in the quarterly DHS Pulse Surveys. Results of extracted topics/themes are provided to DHS Leadership to better inform agency-wide efforts to meet employees’ basic needs and improve job satisfaction
Topic area: Admin Functions
Development stage: Deployed
First used: 2022-11-01
US federal AI use case: Email Analytics for Investigative Data
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the time-consuming and resource-intensive process of preparing multilingual email data for analysis.
Purpose and benefits: Homeland Security Investigations (HSI) personnel encounter large volumes of legally acquired, multilingual email data that must be prepared (ingested, triaged, translated, searched and filtered) before it can be analyzed to support investigations. The email analytics workflow eliminates manual data preparation processes, and leverages machine learning to conduct spam message classification, translation, and entity extraction, including names, organizations, or locations. It also utilizes HSI's AI-enabled translation capabilities (see related use case “Translation and Transcription for Investigative Data”) for translation of emails in other languages to English. The output reduces time and resources spent preparing data, increases the analytic utility of the data, and allows HSI personnel to more quickly conduct analysis on the information.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case provides more efficient data processing for HSI personnel. The AI output may be used to produce investigative insights in the form of data, information leads or connections that HSI personnel can use to inform investigations, but the output itself is data preparation and organization so HSI personnel can produce those leads when combining the AI output with the personnel’s expertise and other relevant investigative data and information. Personnel may use these insights for law enforcement purposes in ongoing investigations with existing targets to assist in activities such as producing risk assessments about individuals or identifying criminal suspects; however, all insights are reviewed and validated by both personnel and supervisors before being included in any official case management system, and do not serve as a principal basis for law enforcement action or decision. Any enforcement decisions related to these insights are outcomes of the full Federal Investigation Process involving verifying any insights as evidence (including validating AI-translated material by a certified interpreter), presentation to a U.S. Attorney’s Office and potentially a District Court judge, decision to prosecute, judicial review, and trial and sentencing.
US federal AI use case: Mobile Device Analytics for Investigative Data
Agency: Department of Homeland Security - ICE
Development stage: Retired
Risk category: Presumed high-impact (determined not high)
Impact assessment: The definition states that the "output serves as a principal basis for a decision or action concerning a specific individual or entity..."  However, the output of the AI is never the only basis for a decision, and no rights impacting action is taken unless a human is in the loop making a further determatination
US federal AI use case: Confidence Scoring for Cybersecurity Threat Indicators
Agency: Department of Homeland Security - CISA
Development stage: Retired
US federal AI use case: Identification Card and Travel Document Code Detection
Agency: Department of Homeland Security - ICE
Development stage: Retired
US federal AI use case: Person-Centric Identity Services Deduplication Model
Agency: Department of Homeland Security - USCIS
Problem addressed: Critical to the success of PCIS is the entity resolution and de-deduplication of individual records from various systems of records to create a complete picture of a person. Using machine learning (ML), the model can identify which case management records belong to the same unique individual with a high degree of confidence. This allows PCIS to compile a full immigration history for an individual without the need for time-consuming research across multiple disparate systems. The de-duplication model plays a critical role in the entity resolution and surfacing of a person and all their associated records. The ML models are more resilient to fuzzy matches and handle varying data fill rates more reliably.
Purpose and benefits: Using Machine Learning allows us to improve entity resolution as compared to rule based system.

PCIS offers the ability to see a person's immigration history organized in one place. Specific benefits do or will include: an organized summary view of the identity with the individual's latest photo from PCIS; full immigration history including receipts associated with the applicant, regardless of case management system; mailing, physical, and safe history of the individual organized in reverse chronological order, allowing users to easily find the most recent address; and all identifiers associated with the applicant, including A-Numbers, FINs, SSNs, SSNs, ELIS account numbers, passport numbers, etc.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2023-02-01
US federal AI use case: Person-Centric Identity Services Information Compilation Check
Agency: Department of Homeland Security - USCIS
Problem addressed: The output of the use case is the numerical confidence score which is used to determine the validity of the A-number presented in search results. The confidence score identifies which records from within PCIS best match search criteria for an A-number.
Purpose and benefits: The aim of this use case is to leverage machine learning to test the accuracy of PCIS to identify and manage associations between individuals and their assigned A-numbers, which is a unique 7, 8, or 9 digit number assigned to a noncitizen by DHS.  The A-Number plays a critical role in surfacing of a person and all their associated records from across PCIS.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case verifies the ongoing accuracy of information compiled from within the Person-Centric Identity Services (PCIS). It identifies which records from within PCIS best match search criteria to support case processing.

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The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services.

The use case compiles records from across a variety of USCIS systems to provide a comprehensive history of a person’s interaction with USCIS. The output of this can be visualized through a report or dashboard to assist with case review ensuring access to helpful and accurate records. Adjudicators review the outputs of this use case, alongside other information and insights, to process a case and make a final determination. The adjudication process can be conducted without this tool, however, doing so would significantly increase the time and effort required to process immigration requests.
First used: 2022-07-01
US federal AI use case: Identity Match Option (IMO) Tool for Record Compilation
Agency: Department of Homeland Security - USCIS
Development stage: Retired
Risk category: Presumed high-impact (determined not high)
Impact assessment: The use case compiles records from across a variety of USCIS systems to provide a comprehensive history of a person’s interaction with USCIS. The AI output can be visualized through a report or dashboard to assist with case review ensuring access to useful and accurate records. (SEE DHS CAIO SUPER MEMO FY24) 

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The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services.

The use case compiles records from across a variety of USCIS systems to provide a comprehensive history of a person’s interaction with USCIS. The output of this can be visualized through a report or dashboard to assist with case review ensuring access to helpful and accurate records. Adjudicators review the outputs of this use case, alongside other information and insights, to process a case and make a final determination. The adjudication process can be conducted without this tool, however, doing so would significantly increase the time and effort required to process immigration requests.
US federal AI use case: Aircraft Landing Location Predictor (KESTREL)
Agency: Department of Homeland Security - CBP
Problem addressed: Monitoring activities in the air and maritime domains to identify unusual patterns or behaviors.
Purpose and benefits: The system analyzes potential landing locations for aircraft to support response planning and preparation.
Topic area: Transportation
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI predicts aircraft landing locations.  The AI outputs do not produce an action or serve as a principal basis for a decision that has the potential to significantly impact the safety of human-life or well-being, climate or environment, critical infrastructure, or strategic assets or resources. The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services.
First used: 2022-10-01
US federal AI use case: Empty Container Detection Model (Cargo Insights Team)
Agency: Department of Homeland Security - CBP
Problem addressed: This enhances border security and optimizes resource allocation for inspections. The model is designed to accurately identify and track empty containers in cargo shipments, preventing errors and fraud in cargo declarations.
Purpose and benefits: The AI improves accuracy, enhances efficiency by prioritizing legitimate containers for inspection, and strengthens security by detecting potential smuggling risks.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2023-06-24
US federal AI use case: Commodity Detection Model (Cargo Insights Team)
Agency: Department of Homeland Security - CBP
Problem addressed: This enhances border security and optimizes resource allocation for inspections. Analyze X-Ray images and predict the commodity code, reducing the need for users to manually enter commodity codes.
Purpose and benefits: This project leverages computer vision with object detection and a neural network to analyze X-Ray images and predict the commodity code.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2025-01-21
US federal AI use case: Traveler Entity Resolution
Agency: Department of Homeland Security - CBP
Problem addressed: Traveler Entity Resolution AI/ML models aim to improve both security and operational efficiency by focusing on individuals who may present higher risks, by improving the certainty of traveler record matches to assist CBP personnel in identifying suspicious travelers for follow-on action.
Purpose and benefits: To enhance the efficiency and effectiveness of screening passengers for potential security risks. The AI model assess traveler data such as travel patterns and historical records assisting CBP personnel to prioritize higher-risk individuals for further screening, streamlining the vetting process, allowing CBP personnel to focus resources on the most high-risk travelers, thereby improving border security while reducing the burden of manual screening.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2012-12-01
US federal AI use case: Passport Anomaly Model
Agency: Department of Homeland Security - CBP
Problem addressed: The Passport Anomaly Model addresses challenges stemming from the lack of formal notification regarding updates to passport series, such as issuance of new series or expiration of old ones. By analyzing historical trends, the model evaluates whether a passport exhibits typical or atypical characteristics and alerts officers when further scrutiny may be warranted. This capability enhances the integrity of travel document verification by enabling CBP personnel to conduct thorough and efficient reviews, ensuring security and accuracy in the inspection process.
Purpose and benefits: The model assists CBP personnel in passenger targeting and vetting by analyzing anomalies in Electronic System for Travel Authorization (ESTA) and non-ESTA country-specific traveler passports to improve the accuracy of matching and streamlining the screening process by reducing errors and enhancing the identification of high-risk travelers.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services.
The AI output is an assessment of passport validity in response to an officer's request for passport validation. This result could be related to an inconsistency or abnormality in a passport's pattern. This is a tool available to CBP officers for confirming the validity of a passport. This result is used to notify the CBP officer that a passport may require review, as it may be part of a newly released sequence, may be invalid, or even possibly fraudulent. This is only one piece of information provided to CBP Officers during the normal course of their duties. The officers would use any results provided to research the validity of the passport through other sources.
First used: 2017-07-01
US federal AI use case: Agriculture Commodity Model (AGC)
Agency: Department of Homeland Security - CBP
Problem addressed: AI/ML Model aids CBP personnel in accurately and efficiently identifying cargo shipments at risk for agricultural pests in compliance with APTL's agricultural monitoring program. Due to the excessive volume and velocity of inbound cargo shipments, CBP personnel cannot possibly evaluate every shipment for risk. AI/ML models assist in performing a greater depth of risk assessment across all inbound cargo shipments to identify the most likely ones that would require additional attention, analysis and possible examination.
Purpose and benefits: The AGC Model uses data analytics and risk indicators to prioritize inspections and allocate resources effectively. This proactive approach helps protect the U.S. food supply and agricultural economy while facilitating legitimate trade.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI outputs do not produce an action or serve as a principal basis for a decision that has the potential to significantly impact the safety of human-life or well-being, climate or environment, critical infrastructure, or strategic assets or resources. The use case identifies agricultural pest risk in cargo shipments entering the United States. If a cargo shipment is identified as high-risk for pest infestation, it is prioritized for inspection. The model does not track or identify individuals.
First used: 2022-07-01
US federal AI use case: Machine Translation
(Previously Language Translator)
Agency: Department of Homeland Security - ICE
Development stage: Retired
US federal AI use case: Cargo Classification Tool
Agency: Department of Homeland Security - CBP
Problem addressed: The need to improve data classification, enable better machine learning integration, and facilitate nuanced trade entity risk assessments. By categorizing goods based on their descriptions and characteristics, these classifiers help identify potential threats associated with specific cargo types associated with prior violations.
Purpose and benefits: CBP’s Cargo Classification Tool improves trade compliance and enhances cargo risk assessment by streamlining the classification of goods, enabling better integration with machine learning systems, and refining entity risk evaluations. It identifies potential threats linked to specific cargo types and prior violations by categorizing goods based on their descriptions and attributes. These improvements contribute to faster, more accurate classification and risk-based targeting, which strengthens security and facilitates trade.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2020-10-01
US federal AI use case: Trade Entity Risk Model
Agency: Department of Homeland Security - CBP
Problem addressed: The need to continuously assess and identify trade entity risk to help better assess cargo threats.
Purpose and benefits: The Trade Entity Risk model enhances existing predictive threat models by compiling a risk profile that includes historical transaction data, relationships with trading partners, and relevant compliance information. This aggregated data helps create measurable risk indicators for trade entities.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The Trade Entity Risk model/tool enhances cargo predictive threat models by providing a comprehensive risk profile that aggregates historical trade entity transactions, trading partner relationships, reviews, examinations, and violations (within CBP data holdings) to create quantifiable risk measures for all trade entities. The AI model serves as an input to larger AI/ML cargo risk targeting models to better assess cargo threats and inform focus areas for trade targeting. Its outputs are not directly shared with users or operators and the output does not serve as a principal basis for decision or actions.
First used: 2025-07-15
US federal AI use case: Normalization Services
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of data redundancies, inconsistencies difficult to integrate data that hinders the efficiency and accuracy of investigations.
Purpose and benefits: HSI utilizes artificial intelligence to enhance data accuracy and efficiency by verifying, validating, correcting, and normalizing various types of information, including addresses, phone numbers, names, and ID numbers. This process helps to eliminate data entry errors, detect intentional misidentification, and connect related information across multiple datasets, ultimately reducing the time and resources required for investigations. The machine learning-powered normalization services offered by HSI include converting ambiguous addresses into usable formats, identifying ID types from partial information, categorizing names with complex suffixes and family names, and standardizing phone numbers to the E164 format, including determining their originating county. By normalizing and improving the quality of investigative datasets, HSI is able to use more advanced tools to find correlations and leads that would have otherwise gone undetected without extensive manual effort.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2021-04-01
US federal AI use case: AI for Autonomous Situational Awareness
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: GenAI for Data Platform Operations
Agency: Department of the Treasury - Technology Common Services Center (TCSC)
Problem addressed: Helps developers build  applications by providing scalable access to large language models for tasks such as natural language processing, code generation, and automation. It integrates with other services to allow for enterprise-grade deployment of AI-powered solutions.
Purpose and benefits: AI solution allow EDM to enhance  services by automating communication and data analysis, and improving decision-making. It e reduces administrative burden and supports transparency by summarizing complex information.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The AI system's outcome does not affect civil rights, safety, or access to critical services, therefore, it doesn't meet the criteria for a high-impact AI use case.
US federal AI use case: Meeting Transcription Summarization
Agency: Department of Commerce - BEA
US federal AI use case: LLM support for NIST research (Azure)
Agency: Department of Commerce - NIST
US federal AI use case: Predicting annual market squid returns using machine learning methods
Agency: Department of Commerce - NOAA
US federal AI use case: AI-based automation of acoustic detection of marine mammals
Agency: Department of Commerce - NOAA
US federal AI use case: Passive acoustic analysis using ML in Cook Inlet, AK
Agency: Department of Commerce - NOAA
US federal AI use case: Automated matching of identification photographs of Cook Inlet beluga whales
Agency: Department of Commerce - NOAA
US federal AI use case: Automate detection of marine mammals and birds in still images
Agency: Department of Commerce - NOAA
US federal AI use case: Automated detections of fish and invertebrates in Habcam images
Agency: Department of Commerce - NOAA
US federal AI use case: Deep learning algorithms to automate right whale photo id
Agency: Department of Commerce - NOAA
US federal AI use case: Ropeless Geolocation System
Agency: Department of Commerce - NOAA
US federal AI use case: Capitalizing on a groundfish image library to test automated image
classification in the northeast region.
Agency: Department of Commerce - NOAA
US federal AI use case: Geospatial Artificial Intelligence for Animals (GAIA)
Agency: Department of Commerce - NOAA
US federal AI use case: Science Data Portal Autosuggest Search
Agency: Department of Commerce - NIST
US federal AI use case: Robotic microscopes and machine learning algorithms remotely and autonomously track lower trophic levels for improved ecosystem monitoring and assessment
Agency: Department of Commerce - NOAA
US federal AI use case: Advancing sustainable shellfish aquaculture through machine learning and automated data
collection on fish communities
Agency: Department of Commerce - NOAA
US federal AI use case: Integrating AI into AUV image analysis workflow
Agency: Department of Commerce - NOAA
US federal AI use case: Development of large annotated image data sets for training detection of groundfish and benthic invertebrates
Agency: Department of Commerce - NOAA
US federal AI use case: Using CoralNet to develop subtrate detection models for AUV imagery
Agency: Department of Commerce - NOAA
US federal AI use case: AI and Machine Learning for end-to-end marine ecosystem model calibration and validation
Agency: Department of Commerce - NOAA
US federal AI use case: Climate Change Impacts on the California Current Marine Ecosystem
Agency: Department of Commerce - NOAA
US federal AI use case: Puget Sound Climate Impacts on Orcas and Salmon
Agency: Department of Commerce - NOAA
US federal AI use case: Automating Anadromous Fish Counts using imaging sonar data
Agency: Department of Commerce - NOAA
US federal AI use case: VIAME: Video and Image Analysis for the Marine Environment SoftwareToolkit
Agency: Department of Commerce - NOAA
US federal AI use case: Library Market Research GenAI Tools Pilot
Agency: Department of Commerce - NIST
US federal AI use case: Artificial Fintelligence: Automating photo-ID of dolphins in the Pacific Islands
Agency: Department of Commerce - NOAA
US federal AI use case: Machine learning to automate review of electronic monitoring data collected from the Hawaii Longline Fisheries
Agency: Department of Commerce - NOAA
US federal AI use case: An Interactive Machine Learning Toolkit for Classifying Species Identity of Cetacean Echolocation Signals in Passive Acoustic Recordings
Agency: Department of Commerce - NOAA
US federal AI use case: Advancing the use of technology for port sampling in the US Caribbean using image analysis for length composition
Agency: Department of Commerce - NOAA
US federal AI use case: Fast tracking the use of VIAME for automated identification of reef fish
Agency: Department of Commerce - NOAA
US federal AI use case: Automation detection of sea turtles from Uncrewed Aircraft System (UAS) Surveys
Agency: Department of Commerce - NOAA
US federal AI use case: AI for automated Rice's whale call detections and soundscape sources
Agency: Department of Commerce - NOAA
US federal AI use case: Using FinFindR (computer-assisted identification of dorsal fins) for automation of photo processing.
Agency: Department of Commerce - NOAA
US federal AI use case: Developing automation in the shrimp fisheries electronic monitoring.
Agency: Department of Commerce - NOAA
US federal AI use case: Developing image library for EM collected data to ID Protected Species Bycatch.
Agency: Department of Commerce - NOAA
US federal AI use case: Google Gemini
Agency: Department of Commerce - NIST
US federal AI use case: Developing automation in the Reefish fisheries electronic monitoring.
Agency: Department of Commerce - NOAA
US federal AI use case: Automation and detection of Marine Mammals and Turtles from AUV collected imagery.
Agency: Department of Commerce - NOAA
US federal AI use case: Integrating query learning and domain adaptation to develop robust ML algrithms to determine species and count using optical data gathered from fisheries dependent and fisheries independent data collected in the Gulf of Mexico.
Agency: Department of Commerce - NOAA
US federal AI use case: Developing deep learning models to automate age determination
Agency: Department of Commerce - NOAA
US federal AI use case: Using community-sourced underwater photography and image recognition software to study green sea turtle distribution and ecology in southern California
Agency: Department of Commerce - NOAA
US federal AI use case: Searching for large whales in UAS photographic strip transect images: developing an AI/ML object detection model using aerial photogrammetry catalogues
Agency: Department of Commerce - NOAA
US federal AI use case: Automated whale blow detections using IR cameras
Agency: Department of Commerce - NOAA
US federal AI use case: Partially automated matching of gray whales in lateral photo identification images
Agency: Department of Commerce - NOAA
US federal AI use case: California sea lion, Steller sea lion, and northern fur seal automated count program in the California current
Agency: Department of Commerce - NOAA
US federal AI use case: Uncertainties and recommendations for projecting species distributions under climate change
Agency: Department of Commerce - NOAA
US federal AI use case: LLM support for NIST research (NIST HPC)
Agency: Department of Commerce - NIST
US federal AI use case: Quantifying the spatiotemporal overlap of albacore with diverse fisheries and IUU risk factors in the North Pacific
Agency: Department of Commerce - NOAA
US federal AI use case: Advancing the West Coast Ocean Forecasting System through Assessment, Model Development, and Ecological Products
Agency: Department of Commerce - NOAA
US federal AI use case: Dynamic prediction system for illegal, unregulated, and unreported fishing
Agency: Department of Commerce - NOAA
US federal AI use case: Where did they not go? Considerations for generating pseudo-absences for telemetry-based habitat models
Agency: Department of Commerce - NOAA
US federal AI use case: Predictability of Species Distributions Deteriorates Under Novel Environmental Conditions in the California Current System
Agency: Department of Commerce - NOAA
US federal AI use case: Denoising Citizen Science  Big-Data - Empowering Magnetic Navigation with Machine Learning
Agency: Department of Commerce - NOAA
US federal AI use case: SUVI Thematic Maps
Agency: Department of Commerce - NOAA
US federal AI use case: Use of AI/ML CNN for VIIRS cloud clearing and super resolution
Agency: Department of Commerce - NOAA
US federal AI use case: The Development of ProbSevere v3 - An improved nowcasting model in support of severe weather warning operations
Agency: Department of Commerce - NOAA
US federal AI use case: The VOLcanic Cloud Analysis Toolkit (VOLCAT): An application system for detecting, tracking, characterizing, and forecasting hazardous volcanic events
Agency: Department of Commerce - NOAA
US federal AI use case: LLM support for NIST research (Google Vertex)
Agency: Department of Commerce - NIST
US federal AI use case: Automated detection of hazardous low clouds in support of safe and efficient transportation
Agency: Department of Commerce - NOAA
US federal AI use case: The Next Generation Fire System (NGFS): Automated human expert-like detection of fires in satellite imagery
Agency: Department of Commerce - NOAA
US federal AI use case: Nowcasting Extreme Fire Behavior
Agency: Department of Commerce - NOAA
US federal AI use case: Leveraging Machine Learning to Enhance the Quality of Ocean Observations
Agency: Department of Commerce - NOAA
US federal AI use case: Work with Allen Institute on developing the Ai2 Climate Emulator (ACE) for seamless weather applications, including to emulate SHiELD-based medium-range forecasts for large ensemble predictions
Agency: Department of Commerce - NOAA
US federal AI use case: Work with NMFS and NOS using AI/ML to understand how fish habitats are shaped by ocean conditions, and how changes in conditions might impact fisheries distributions and productivity.
Agency: Department of Commerce - NOAA
US federal AI use case: AI/ML techniques to infer local climate conditions based on large-scale climate drivers (i.e., empirical-statistical downscaling)
Agency: Department of Commerce - NOAA
US federal AI use case: Use of AI/ML techniques to understand the factors controlling coastal hypoxia and its predictabilit
Agency: Department of Commerce - NOAA
US federal AI use case: A Hybrid Data-driven and Physics-based Framework for Atmospheric Radiative Transfer Modeling
Agency: Department of Commerce - NOAA
US federal AI use case: Detection of hardware issues in complex, wide-area computing systems based on non-intrusive workflow performance data gathered via the GFDL EPMT
Agency: Department of Commerce - NOAA
US federal AI use case: Community-based Messaging with LLM
Agency: Department of Commerce - NOAA
US federal AI use case: AI based Precipitation estimation
Agency: Department of Commerce - NOAA
US federal AI use case: Optimization of highly-concurrent workflow task management systems based on anomaly detection using non-intrusive workflow performance data gathered via the GFDL EPMT
Agency: Department of Commerce - NOAA
US federal AI use case: Weather.gov 2.0 Rebuild and NWS API Improvement Efforts
Agency: Department of Commerce - NOAA
US federal AI use case: Utilizing Neural Operator Deep Learning to Enhance National-Scale Coastal Ocean Predictions
Agency: Department of Commerce - NOAA
US federal AI use case: Physics-Informed Neural Network Hurricane Vortex Reconstruction
Agency: Department of Commerce - NOAA
US federal AI use case: A hybrid physics-machine learning model for orographic precipitation forecasting
Agency: Department of Commerce - NOAA
US federal AI use case: Observation-Centric Estimation and Learning for Outlook Trajectories (OCELOT)
Agency: Department of Commerce - NOAA
US federal AI use case: Severe Convective Weather Parameter Generation using AI/ML with Microwave/Infrared Sounder Satellite Observations for Enhanced Weather Analysis and Forecast
Agency: Department of Commerce - NOAA
US federal AI use case: AI Based Calibration/validation of satellite microwave sounder observations for Numerical Weather Prediction (NWP)
Agency: Department of Commerce - NOAA
US federal AI use case: AI/ML Enterprise Cloud Mask development and operational implementation for all NOAA and international partner sensors
Agency: Department of Commerce - NOAA
US federal AI use case: NWS Mutual Aid Coordination
Agency: Department of Commerce - NOAA
US federal AI use case: AI-powered Chatbot for Federal Funding Assistance (Grants) Guidance Dissemination
Agency: Department of Commerce - NOAA
US federal AI use case: Essential Fish Habitat Consultation Efficiency Increases and Template Creation
Agency: Department of Commerce - NOAA
US federal AI use case: Generative AI for Biological Opinions
Agency: Department of Commerce - NOAA
US federal AI use case: Administrative Tools - (Such as meeting/document management, reasonable accommodation needs, other administrative efficiencies such as broad code generation/translation/optimizations)
Agency: Department of Commerce - NOAA
US federal AI use case: CED Generative AI Pilot Program
Agency: Department of Commerce - NOAA
US federal AI use case: GitHub CoPilot
Agency: Department of Commerce - NOAA
US federal AI use case: Document existing functions
Agency: Department of Commerce - NOAA
US federal AI use case: LLM-Generated Causal Models
Agency: Department of Commerce - NOAA
US federal AI use case: SWFSC Publications Search
Agency: Department of Commerce - NOAA
US federal AI use case: Using ChatGPT4 /DALLE3, Adobe Sensei and similar for design ideation and image generation
Agency: Department of Commerce - NOAA
US federal AI use case: Draft Fronts for Surface Analysis
Agency: Department of Commerce - NOAA
US federal AI use case: Coastal Zone Management Act Section 312 Evaluations AI Pilot Project
Agency: Department of Commerce - NOAA
US federal AI use case: Support Chat Bot
Agency: Department of Commerce - NOAA
US federal AI use case: A Study to Determine Natural Language Processing (NLP) Capabilities with the NCCF Open Knowledge Mesh
Agency: Department of Commerce - NOAA
US federal AI use case: Developing Access Capabilities for the NCCF Open Information Stewardship Service (OISS)
Agency: Department of Commerce - NOAA
US federal AI use case: Digital Twin for Earth Observations Using Artificial Intelligence
Agency: Department of Commerce - NOAA
US federal AI use case: Improving Imagery Visualization using Limb-Correction and AI Resolution Enhancement for Microwave sensors
Agency: Department of Commerce - NOAA
US federal AI use case: Super-resolution of Satellite Imagery Products using Generative AI
Agency: Department of Commerce - NOAA
US federal AI use case: Ocean AI
Agency: Department of Commerce - NOAA
US federal AI use case: AI based radiative transfer emulator for data assimilation and remote sensing
Agency: Department of Commerce - NOAA
US federal AI use case: Integrating NOAA APIs with LLMs for Enhanced Access to Environmental Data and Insights
Agency: Department of Commerce - NOAA
US federal AI use case: Enhance LSR (Local Storm Report) Creation
Agency: Department of Commerce - NOAA
US federal AI use case: AI Pair Programming with GitHub CoPilot
Agency: Department of Commerce - NOAA
US federal AI use case: Gemini (Previously Duet AI)
Agency: Department of Commerce - NOAA
US federal AI use case: AI-Driven Predictive Maintenance for the NOAA Fleet
Agency: Department of Commerce - NOAA
US federal AI use case: AI-Enhanced Emergency Response and Mission Continuity Planning
Agency: Department of Commerce - NOAA
US federal AI use case: Emissions Reduction for NOAA Fleet
Agency: Department of Commerce - NOAA
US federal AI use case: Fleet Requirements Analysis and Management Engine (FRAME)
Agency: Department of Commerce - NOAA
US federal AI use case: Assissted translation of code between Matlab, R, and Python
Agency: Department of Commerce - NOAA
US federal AI use case: Storm Events Knowledge Graph Chatbot
Agency: Department of Commerce - NOAA
US federal AI use case: Alma/Primo
Agency: Department of Commerce - NOAA
US federal AI use case: Amazon Q Developer Pilot
Agency: Department of Commerce - NOAA
US federal AI use case: FirstNet Authority Network Program Management Data Analytics Tool and Service
Agency: Department of Commerce - FirstNet
US federal AI use case: NWS Public Safety Language Translation
Agency: Department of Commerce - NOAA
US federal AI use case: APIgee with Gemini Code Assist for OpenAPI Development
Agency: Department of Commerce - NOAA
US federal AI use case: Grants Program Administration - Inquiry management Chatbot Pilot
Agency: Department of Commerce - NTIA