{"description":"Documents matching '\"machine learning\"'","count":489,"total_pages":25,"next_page_url":"https://www.federalregister.gov/api/v1/documents?conditions%5Bterm%5D=%22machine+learning%22&format=json&page=2","results":[{"title":"Medical Devices; Radiology Devices; Classification of the Radiological Machine Learning-Based Quantitative Imaging Software With Predetermined Change Control Plan","type":"Rule","abstract":"The Food and Drug Administration (FDA) is classifying the radiological machine learning-based quantitative imaging software with predetermined change control plan into class II (special controls). The special controls that apply to the device type are identified in this order and will be part of the codified language for classification of the radiological machine learning-based quantitative imaging software with predetermined change control plan. We are taking this action because we have determined that classifying the device into class II will provide a reasonable assurance of safety and effectiveness of the device. We believe this action will also enhance patients' access to beneficial innovative devices, in part by reducing regulatory burdens.","document_number":"2026-12166","html_url":"https://www.federalregister.gov/documents/2026/06/17/2026-12166/medical-devices-radiology-devices-classification-of-the-radiological-machine-learning-based","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-06-17/pdf/2026-12166.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-12166.pdf?1781613912","publication_date":"2026-06-17","agencies":[{"raw_name":"DEPARTMENT OF HEALTH AND HUMAN SERVICES","name":"Health and Human Services Department","id":221,"url":"https://www.federalregister.gov/agencies/health-and-human-services-department","json_url":"https://www.federalregister.gov/api/v1/agencies/221","parent_id":null,"slug":"health-and-human-services-department"},{"raw_name":"Food and Drug Administration","name":"Food and Drug Administration","id":199,"url":"https://www.federalregister.gov/agencies/food-and-drug-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/199","parent_id":221,"slug":"food-and-drug-administration"}],"excerpts":"ACTION: \n Final amendment; final order. \n \n \n SUMMARY: \n The Food and Drug Administration (FDA) is classifying the radiological <span class=\"match\">machine learning</span>-based quantitative imaging software with predetermined change control plan into class II (special controls). The special controls that apply to the device type are identified in this order and will be part of the codified language for classification of the radiological <span class=\"match\">machine learning</span>-based quantitative imaging software with predetermined change control plan. We are taking this action because we have determined"},{"title":"Public Meeting; Center of Independent Experts Review of the Machine Learning Coupled With Fourier Transform Near-Infrared Spectroscopy of Otoliths to Age Fish","type":"Notice","abstract":"The Center of Independent Experts (CIE) review of the Machine Learning Coupled with Fourier Transform Near-infrared Spectroscopy of Otoliths to Age Fish will be held February 11, 2025, through February 13, 2025.","document_number":"2025-02023","html_url":"https://www.federalregister.gov/documents/2025/01/31/2025-02023/public-meeting-center-of-independent-experts-review-of-the-machine-learning-coupled-with-fourier","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2025-01-31/pdf/2025-02023.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2025-02023.pdf?1738244713","publication_date":"2025-01-31","agencies":[{"raw_name":"DEPARTMENT OF COMMERCE","name":"Commerce Department","id":54,"url":"https://www.federalregister.gov/agencies/commerce-department","json_url":"https://www.federalregister.gov/api/v1/agencies/54","parent_id":null,"slug":"commerce-department"},{"raw_name":"National Oceanic and Atmospheric Administration","name":"National Oceanic and Atmospheric Administration","id":361,"url":"https://www.federalregister.gov/agencies/national-oceanic-and-atmospheric-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/361","parent_id":54,"slug":"national-oceanic-and-atmospheric-administration"}],"excerpts":"ACTION: \n Notice of hybrid meeting. \n \n \n SUMMARY: \n \n The Center of Independent Experts (CIE) review of the <span class=\"match\">Machine Learning</span> Coupled with Fourier Transform Near-infrared Spectroscopy of Otoliths to Age Fish will be held \n \n February 11, 2025, through February 13, 2025.\n \n \n \n DATES: \n The meeting will be held on Tuesday, February 11, 2025, through Thursday, February 13, 2025, from 9 a.m. to 5 p.m. Pacific Time. \n \n \n ADDRESSES: \n \n The meeting will be a hybrid meeting. The in-person component of the meeting will be held at the Alaska Fisheries"},{"title":"Solicitation of Nominations for Membership on the NOAA Science Advisory Board (SAB)","type":"Notice","abstract":"The NOAA Science Advisory Board (SAB) is the only Federal Advisory Committee with responsibility to advise the Under Secretary of Commerce for Oceans and Atmosphere on long- and short-range strategies for research, education and the application of science to resource management and environmental assessment and prediction. NOAA seeks candidates with expertise in areas relevant to its mission, including Federal, State, and local government; social and behavioral sciences; artificial intelligence and machine learning; high performance computing; data management, including open access and accessibility; uncrewed systems; whale research and conservation; aircraft systems and modernization; and economic analysis, including cost-benefit evaluation of observing systems. NOAA also encourages nominations of qualified mid-career scientists and engineers.","document_number":"2026-16884","html_url":"https://www.federalregister.gov/documents/2026/08/19/2026-16884/solicitation-of-nominations-for-membership-on-the-noaa-science-advisory-board-sab","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-08-19/pdf/2026-16884.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-16884.pdf?1787057114","publication_date":"2026-08-19","agencies":[{"raw_name":"DEPARTMENT OF COMMERCE","name":"Commerce Department","id":54,"url":"https://www.federalregister.gov/agencies/commerce-department","json_url":"https://www.federalregister.gov/api/v1/agencies/54","parent_id":null,"slug":"commerce-department"},{"raw_name":"National Oceanic and Atmospheric Administration","name":"National Oceanic and Atmospheric Administration","id":361,"url":"https://www.federalregister.gov/agencies/national-oceanic-and-atmospheric-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/361","parent_id":54,"slug":"national-oceanic-and-atmospheric-administration"}],"excerpts":"application of science to resource management and environmental assessment and prediction. NOAA seeks candidates with expertise in areas relevant to its mission, including Federal, State, and local government; social and behavioral sciences; artificial intelligence and <span class=\"match\">machine learning</span>; high performance computing; data management, including open access and accessibility; uncrewed systems; whale research and conservation; aircraft systems and modernization; and economic analysis, including cost-benefit evaluation of observing systems. \n NOAA also encourages"},{"title":"Government Owned Inventions Available for Licensing or Collaboration: Machine Learning Model for the Prioritization of Cancer Neoepitopes","type":"Notice","abstract":"The National Cancer Institute (NCI), an institute of the National Institutes of Health (NIH), Department of Health and Human Services (HHS), is giving notice of licensing and collaboration opportunities for the inventions listed below, which are owned by an agency of the U.S. Government and are available for license and collaboration in the U.S. to achieve expeditious commercialization of results of federally-funded research and development.","document_number":"2024-26464","html_url":"https://www.federalregister.gov/documents/2024/11/14/2024-26464/government-owned-inventions-available-for-licensing-or-collaboration-machine-learning-model-for-the","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2024-11-14/pdf/2024-26464.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2024-26464.pdf?1731505542","publication_date":"2024-11-14","agencies":[{"raw_name":"DEPARTMENT OF HEALTH AND HUMAN SERVICES","name":"Health and Human Services Department","id":221,"url":"https://www.federalregister.gov/agencies/health-and-human-services-department","json_url":"https://www.federalregister.gov/api/v1/agencies/221","parent_id":null,"slug":"health-and-human-services-department"},{"raw_name":"National Institutes of Health","name":"National Institutes of Health","id":353,"url":"https://www.federalregister.gov/agencies/national-institutes-of-health","json_url":"https://www.federalregister.gov/api/v1/agencies/353","parent_id":221,"slug":"national-institutes-of-health"}],"excerpts":"NCI created a novel approach to identify and prioritize patient neoantigens. This model uses a training dataset of known neoantigens from patient screening and determines features of importance to epitope recognition using both reactive and non-reactive epitopes. The <span class=\"match\">machine learning</span> algorithm scores epitopes for their likelihood of reactivity and provides a stable, reproducible method to prioritize epitopes that can be used anywhere. \n This Notice is in accordance with 35 U.S.C. 209 and 37 CFR part 404. \n \n NIH Reference Number: \n E-022-2024-0."},{"title":"Cooperative Research and Development Agreement: Payload Incorporated With Computer Vision and Machine Learning","type":"Notice","abstract":"The Coast Guard is announcing its intent to enter into a Cooperative Research and Development Agreement (CRADA) with AeroVironment, Inc. to evaluate payload(s) that can accelerate autonomy to fielded assets and uncrewed platforms, and automated overhead imagery analysis tool software. The Coast Guard is currently considering partnering with AeroVironment, Inc. to investigate their payload that seamlessly integrates with current AeroVironment UAS in use by the Coast Guard and solicits public comment on the possible participation of other parties in the proposed CRADA, and the nature of that participation. While the Coast Guard is currently considering partnering with AeroVironment, Inc., we are soliciting public comment on the possible nature of and participation of other parties in the proposed CRADA. In addition, the Coast Guard also invites other potential Federal participants, who have the interest and capability to bring similar contributions to this type of research, to consider submitting proposals for consideration in similar CRADAs.","document_number":"2024-13926","html_url":"https://www.federalregister.gov/documents/2024/06/26/2024-13926/cooperative-research-and-development-agreement-payload-incorporated-with-computer-vision-and-machine","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2024-06-26/pdf/2024-13926.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2024-13926.pdf?1719319518","publication_date":"2024-06-26","agencies":[{"raw_name":"DEPARTMENT OF HOMELAND SECURITY","name":"Homeland Security Department","id":227,"url":"https://www.federalregister.gov/agencies/homeland-security-department","json_url":"https://www.federalregister.gov/api/v1/agencies/227","parent_id":null,"slug":"homeland-security-department"},{"raw_name":"Coast Guard","name":"Coast Guard","id":53,"url":"https://www.federalregister.gov/agencies/coast-guard","json_url":"https://www.federalregister.gov/api/v1/agencies/53","parent_id":227,"slug":"coast-guard"}],"excerpts":"that uses a full range of strategic, operational, and tactical collection methods to dwell on and revisit a target. \n In the Coast Guard Strategic Plan, rapidly advancing technologies, including those in uncrewed platforms, data analytics, artificial intelligence, and <span class=\"match\">machine learning</span> need to be harnessed for possible use in mission execution. The ability to detect, locate, characterize, identify, and track people or objects in the water in near or real-time and to apply that technology to Coast Guard sensors and systems has the potential to improve"},{"title":"Government Owned Invention Available for License: DNA Methylation-Based Cancer Diagnostics for Accurate Tumor Classification","type":"Notice","abstract":"This technology encompasses a DNA methylation-based diagnostic platform designed to improve the accuracy and consistency of cancer classification, with demonstrated utility for tumors of the central nervous system, kidney, and hematopoietic system. By identifying disease-specific methylation signatures, the approach reduces interobserver variability and enhances diagnostic confidence. The central nervous system (CNS) classifier is built from a curated reference set of 16,567 methylation profiles and organizes tumors into 22 families and 133 clinically relevant diagnostic classes, including 21 newly developed methylation classes not represented in other existing tools. Across multiple independent validation cohorts (n = 5,875), the classifier demonstrated robust performance, and in a clinical-impact analysis of 1,204 NIH validation cases, methylation profiling materially influenced final diagnosis in 74.4% of cases by refining, increasing precision, or reclassifying. The CNS classifier was deployed as a user-facing software tool, MethylScape Analysis, which streamlines methylation-based classification workflows for CNS tumors, https://methylscape.ccr.cancer.gov/. The development of multiple specialized classifiers supports a more granular understanding of tumor biology and informed clinical decision-making.","document_number":"2026-16441","html_url":"https://www.federalregister.gov/documents/2026/08/12/2026-16441/government-owned-invention-available-for-license-dna-methylation-based-cancer-diagnostics-for","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-08-12/pdf/2026-16441.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-16441.pdf?1786452318","publication_date":"2026-08-12","agencies":[{"raw_name":"DEPARTMENT OF HEALTH AND HUMAN SERVICES","name":"Health and Human Services Department","id":221,"url":"https://www.federalregister.gov/agencies/health-and-human-services-department","json_url":"https://www.federalregister.gov/api/v1/agencies/221","parent_id":null,"slug":"health-and-human-services-department"},{"raw_name":"National Institutes of Health","name":"National Institutes of Health","id":353,"url":"https://www.federalregister.gov/agencies/national-institutes-of-health","json_url":"https://www.federalregister.gov/api/v1/agencies/353","parent_id":221,"slug":"national-institutes-of-health"}],"excerpts":"trial eligibility. Variability across observers and institutions can lead to additional testing, delays, and inconsistent diagnoses. \n The NCI/Bethesda classifier addresses this gap using DNA methylation patterns as a robust molecular fingerprint. It applies a stratified <span class=\"match\">machine learning</span> framework to extend diagnostic coverage and improve assignment confidence for CNS tumors. The classifier was developed from a rigorously curated reference set of over 16k methylation profiles, structured into 22 tumor families and 133 clinically relevant diagnostic classes"},{"title":"Renewal of Department of War Federal Advisory Committee-U.S. Strategic Command Strategic Advisory Group","type":"Notice","abstract":"The Department of War (DoW) is publishing this notice to announce that it is renewing the U.S. Strategic Command Strategic Advisory Group (USSTRATCOM SAG) as a discretionary Federal advisory committee.","document_number":"2026-08784","html_url":"https://www.federalregister.gov/documents/2026/05/06/2026-08784/renewal-of-department-of-war-federal-advisory-committee-us-strategic-command-strategic-advisory","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-05-06/pdf/2026-08784.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-08784.pdf?1777985111","publication_date":"2026-05-06","agencies":[{"raw_name":"DEPARTMENT OF DEFENSE","name":"Defense Department","id":103,"url":"https://www.federalregister.gov/agencies/defense-department","json_url":"https://www.federalregister.gov/api/v1/agencies/103","parent_id":null,"slug":"defense-department"},{"raw_name":"Office of the Secretary"}],"excerpts":"is composed of members who are eminent authorities in the fields of strategic policy formulation; nuclear weapon design; national command, control, and communications; electromagnetic spectrum operations, intelligence, disruptive technologies (Artificial Intelligence/<span class=\"match\">Machine Learning</span>, Quantum Computing/Sensing, Cyber, Space), and information operations; or other important aspects of the Nation's strategic forces of interest to the DoW.\n \n In selecting members, the DoW seeks to capitalize on recognized talented, innovative private and public sector"},{"title":"Request for Information Regarding Security Considerations for Artificial Intelligence Agents","type":"Notice","abstract":"The Center for AI Standards and Innovation (CAISI), housed within the National Institute of Standards and Technology (NIST) at the Department of Commerce, is seeking information and insights from stakeholders on practices and methodologies for measuring and improving the secure development and deployment of artificial intelligence (AI) agent systems. AI agent systems are capable of taking autonomous actions that impact real-world systems or environments, and may be susceptible to hijacking, backdoor attacks, and other exploits. If left unchecked, these security risks may impact public safety, undermine consumer confidence, and curb adoption of the latest AI innovations. We encourage respondents to provide concrete examples, best practices, case studies, and actionable recommendations based on their experience developing and deploying AI agent systems and managing and anticipating their attendant risks. Responses may inform CAISI's work evaluating the security risks associated with various AI capabilities, assessing security vulnerabilities of AI systems, developing evaluation and assessment measurements and methods, generating technical guidelines and best practices to measure and improve the security of AI systems, and other activities related to the security of AI agent systems.","document_number":"2026-00206","html_url":"https://www.federalregister.gov/documents/2026/01/08/2026-00206/request-for-information-regarding-security-considerations-for-artificial-intelligence-agents","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-01-08/pdf/2026-00206.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-00206.pdf?1767793519","publication_date":"2026-01-08","agencies":[{"raw_name":"DEPARTMENT OF COMMERCE","name":"Commerce Department","id":54,"url":"https://www.federalregister.gov/agencies/commerce-department","json_url":"https://www.federalregister.gov/api/v1/agencies/54","parent_id":null,"slug":"commerce-department"},{"raw_name":"National Institute of Standards and Technology","name":"National Institute of Standards and Technology","id":352,"url":"https://www.federalregister.gov/agencies/national-institute-of-standards-and-technology","json_url":"https://www.federalregister.gov/api/v1/agencies/352","parent_id":54,"slug":"national-institute-of-standards-and-technology"}],"excerpts":"Some of these risks are shared with other kinds of software systems, such as exploitable vulnerabilities in authentication mechanisms or memory management processes. This Request for Information, however, focuses instead on the novel risks that arise from the use of <span class=\"match\">machine learning</span> models embedded within AI agent systems. Within this category are: (1) security risks that arise from adversarial attacks at either training or inference time, when models may interact with potentially adversarial data (\n e.g., \n indirect prompt injection) or may be"},{"title":"Government Owned Inventions Available for License: Gait Assistance Systems and Methods of Control Thereof","type":"Notice","abstract":"The Clinical Center (CC), an institute/center of the National Institutes of Health (NIH), Department of Health and Human Services (HHS), is giving notice of the license opportunity for the invention listed below, which is owned by an agency of the U.S. Government and is available to achieve expeditious commercialization of results of federally-funded research and development.","document_number":"2026-02906","html_url":"https://www.federalregister.gov/documents/2026/02/13/2026-02906/government-owned-inventions-available-for-license-gait-assistance-systems-and-methods-of-control","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-02-13/pdf/2026-02906.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-02906.pdf?1770903914","publication_date":"2026-02-13","agencies":[{"raw_name":"DEPARTMENT OF HEALTH AND HUMAN SERVICES","name":"Health and Human Services Department","id":221,"url":"https://www.federalregister.gov/agencies/health-and-human-services-department","json_url":"https://www.federalregister.gov/api/v1/agencies/221","parent_id":null,"slug":"health-and-human-services-department"},{"raw_name":"National Institutes of Health","name":"National Institutes of Health","id":353,"url":"https://www.federalregister.gov/agencies/national-institutes-of-health","json_url":"https://www.federalregister.gov/api/v1/agencies/353","parent_id":221,"slug":"national-institutes-of-health"}],"excerpts":"treatment of gait pathologies but better methods of controlling such devices/systems to better personalize and adapt them to a patient and provide assistive torque, are needed. \n \n Researchers at the National Institutes of Health Clinical Center have developed an adaptive, <span class=\"match\">machine-learning</span>-based method and associated computing system for generating personalized assistive torque in powered gait assistance systems (\n e.g., \n exoskeletons and orthotic devices). The method employs a multilayer perceptron (MLP) trained on sensor data collected from multiple"},{"title":"Ocean Research Advisory Panel (ORAP)","type":"Notice","abstract":"This notice sets forth the schedule and proposed agenda of a meeting of the Ocean Research Advisory Panel (ORAP). The members will discuss issues outlined in the section on Matters to be Considered.","document_number":"2026-06068","html_url":"https://www.federalregister.gov/documents/2026/03/30/2026-06068/ocean-research-advisory-panel-orap","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-03-30/pdf/2026-06068.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-06068.pdf?1774615516","publication_date":"2026-03-30","agencies":[{"raw_name":"DEPARTMENT OF COMMERCE","name":"Commerce Department","id":54,"url":"https://www.federalregister.gov/agencies/commerce-department","json_url":"https://www.federalregister.gov/api/v1/agencies/54","parent_id":null,"slug":"commerce-department"},{"raw_name":"National Oceanic and Atmospheric Administration","name":"National Oceanic and Atmospheric Administration","id":361,"url":"https://www.federalregister.gov/agencies/national-oceanic-and-atmospheric-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/361","parent_id":54,"slug":"national-oceanic-and-atmospheric-administration"}],"excerpts":"13-14, 2023, the OPC requested that ORAP provide advice on two key areas. The first was to advise on opportunities for partnerships (such as through the National Oceanic Partnership Program) on the topic of emerging technology (which could include Artificial Intelligence/<span class=\"match\">Machine Learning</span>, eDNA, and similar technology) with ocean industry and other sectors over the next 5-10 years. The second request was for ORAP to self-select another topic for consideration. ORAP members determined that the topic of accessible, interoperable, interdisciplinary, and"},{"title":"Agency Information Collection Activities: Request for Comments for a New Information Collection","type":"Notice","abstract":"The FHWA invites public comments about our intention to request the Office of Management and Budget's (OMB) approval for a new information collection, which is summarized below under SUPPLEMENTARY INFORMATION. We are required to publish this notice in the Federal Register by the Paperwork Reduction Act of 1995.","document_number":"2026-07407","html_url":"https://www.federalregister.gov/documents/2026/04/16/2026-07407/agency-information-collection-activities-request-for-comments-for-a-new-information-collection","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-04-16/pdf/2026-07407.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-07407.pdf?1776257119","publication_date":"2026-04-16","agencies":[{"raw_name":"DEPARTMENT OF TRANSPORTATION","name":"Transportation Department","id":492,"url":"https://www.federalregister.gov/agencies/transportation-department","json_url":"https://www.federalregister.gov/api/v1/agencies/492","parent_id":null,"slug":"transportation-department"},{"raw_name":"Federal Highway Administration","name":"Federal Highway Administration","id":170,"url":"https://www.federalregister.gov/agencies/federal-highway-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/170","parent_id":492,"slug":"federal-highway-administration"}],"excerpts":"is researching the current state of practice for digital project delivery, specifically regarding pavement and materials testing and electronic quality assurance (QA) data within State transportation agencies.\n \n While many industries leverage artificial intelligence, <span class=\"match\">machine learning</span>, and robust data analytics to improve decision-making, the highway construction field is seeing an increased need to link disparate data sources. This integration supports various applications, including design models, intelligent construction, e-ticketing, materials"},{"title":"Federal Advisory Committee Act; Technological Advisory Council","type":"Notice","abstract":"In accordance with the Federal Advisory Committee Act, this notice advises interested persons that the Federal Communications Commission's (FCC) Technological Advisory Council will hold a meeting on Tuesday August 5, 2025 in the Commission Meeting Room and available to the public via the internet at http://www.fcc.gov/live, from 10:00 a.m. to 12:30 p.m.","document_number":"2025-13262","html_url":"https://www.federalregister.gov/documents/2025/07/16/2025-13262/federal-advisory-committee-act-technological-advisory-council","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2025-07-16/pdf/2025-13262.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2025-13262.pdf?1752583508","publication_date":"2025-07-16","agencies":[{"raw_name":"FEDERAL COMMUNICATIONS COMMISSION","name":"Federal Communications Commission","id":161,"url":"https://www.federalregister.gov/agencies/federal-communications-commission","json_url":"https://www.federalregister.gov/api/v1/agencies/161","parent_id":null,"slug":"federal-communications-commission"}],"excerpts":"consider and advise the Commission on topics such as continued efforts at looking beyond 5G advanced as 6G begins to develop so as to facilitate U.S. leadership; studying advanced spectrum sharing techniques, including the implementation of artificial intelligence and <span class=\"match\">machine learning</span> to improve the utilization and administration of spectrum; and other emerging technologies. This agenda may be modified at the discretion of the TAC Chair and the Designated Federal Officer (DFO). \n \n Meetings are broadcast live with open captioning over the internet"},{"title":"Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions; Guidance for Industry and Food and Drug Administration Staff; Availability","type":"Notice","abstract":"The Food and Drug Administration (FDA, Agency, or we) is announcing the availability of a final guidance entitled \"Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions.\" This guidance demonstrates FDA's commitment to developing innovative approaches to the regulation of artificial intelligence (AI)-enabled devices. More specifically, this guidance provides recommendations on the information to include in a Predetermined Change Control Plan (PCCP) in a marketing submission for a device that includes one or more AI-enabled device software functions (AI-DSFs). This guidance recommends that a PCCP describe the planned AI-DSF modifications, the associated methodology to develop, validate, and implement those modifications, and an assessment of the impact of those modifications. FDA reviews the PCCP as part of a marketing submission for a device to ensure the continued safety and effectiveness of the device without necessitating additional marketing submissions for implementing each modification described in the PCCP.","document_number":"2024-28361","html_url":"https://www.federalregister.gov/documents/2024/12/04/2024-28361/marketing-submission-recommendations-for-a-predetermined-change-control-plan-for-artificial","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2024-12-04/pdf/2024-28361.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2024-28361.pdf?1733233522","publication_date":"2024-12-04","agencies":[{"raw_name":"DEPARTMENT OF HEALTH AND HUMAN SERVICES","name":"Health and Human Services Department","id":221,"url":"https://www.federalregister.gov/agencies/health-and-human-services-department","json_url":"https://www.federalregister.gov/api/v1/agencies/221","parent_id":null,"slug":"health-and-human-services-department"},{"raw_name":"Food and Drug Administration","name":"Food and Drug Administration","id":199,"url":"https://www.federalregister.gov/agencies/food-and-drug-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/199","parent_id":221,"slug":"food-and-drug-administration"}],"excerpts":"to ensure their safety and effectiveness. As technology continues to advance all facets of healthcare, medical software incorporating AI, including the subset of AI known as <span class=\"match\">machine learning</span> (ML), has become an important part of many medical devices. In April 2019, FDA published the “Proposed Regulatory Framework for Modifications to Artificial Intelligence/<span class=\"match\">Machine Learning</span> (AI/ML)-Based Software as a Medical Device (SaMD)—Discussion Paper and Request for Feedback.” \n 1 \n \n The 2019 discussion paper received a substantial amount of feedback from"},{"title":"Solicitation of Nominations for Membership on the NOAA Science Advisory Board (SAB)","type":"Notice","abstract":"The NOAA Science Advisory Board (SAB) is the only Federal Advisory Committee with responsibility to advise the Under Secretary of Commerce for Oceans and Atmosphere on long- and short-range strategies for research, education and the application of science to resource management and environmental assessment and prediction. The Science Advisory Board is called upon to provide advice to NOAA on a wide variety of topics important to the agency. Because of the breadth of subject matter that the group addresses, they frequently consult with additional experts on specific topics. Certain topics are deemed to be of long-term interest for NOAA and the standing working groups (WGs) were established under the SAB to consult on a regular basis. The SAB WGs consist of experts with whom the SAB consults on a regular basis to address NOAA scientific priorities.","document_number":"2026-16908","html_url":"https://www.federalregister.gov/documents/2026/08/19/2026-16908/solicitation-of-nominations-for-membership-on-the-noaa-science-advisory-board-sab","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-08-19/pdf/2026-16908.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-16908.pdf?1787057117","publication_date":"2026-08-19","agencies":[{"raw_name":"DEPARTMENT OF COMMERCE","name":"Commerce Department","id":54,"url":"https://www.federalregister.gov/agencies/commerce-department","json_url":"https://www.federalregister.gov/api/v1/agencies/54","parent_id":null,"slug":"commerce-department"},{"raw_name":"National Oceanic and Atmospheric Administration (NOAA)","name":"National Oceanic and Atmospheric Administration","id":361,"url":"https://www.federalregister.gov/agencies/national-oceanic-and-atmospheric-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/361","parent_id":54,"slug":"national-oceanic-and-atmospheric-administration"}],"excerpts":"depending on Board needs. \n Scope of Nominations \n NOAA is interested in candidates with expertise in, but not limited to: expertise in areas relevant to its mission, including Federal, State, and local government; social and behavioral sciences; artificial intelligence and <span class=\"match\">machine learning</span>; cloud strategy including high performance computing; commercial data access and management, including open access and accessibility; uncrewed systems; Whale research and conservation; aircraft systems and modernization; and economic analysis, including cost-benefit"},{"title":"Ocean Research Advisory Panel (ORAP)","type":"Notice","abstract":"This notice sets forth the schedule and proposed agenda of a meeting of the Ocean Research Advisory Panel (ORAP). The members will discuss issues outlined in the section on Matters to be Considered.","document_number":"2025-18191","html_url":"https://www.federalregister.gov/documents/2025/09/19/2025-18191/ocean-research-advisory-panel-orap","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2025-09-19/pdf/2025-18191.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2025-18191.pdf?1758199518","publication_date":"2025-09-19","agencies":[{"raw_name":"DEPARTMENT OF COMMERCE","name":"Commerce Department","id":54,"url":"https://www.federalregister.gov/agencies/commerce-department","json_url":"https://www.federalregister.gov/api/v1/agencies/54","parent_id":null,"slug":"commerce-department"},{"raw_name":"National Oceanic and Atmospheric Administration","name":"National Oceanic and Atmospheric Administration","id":361,"url":"https://www.federalregister.gov/agencies/national-oceanic-and-atmospheric-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/361","parent_id":54,"slug":"national-oceanic-and-atmospheric-administration"}],"excerpts":"on December 13-14, 2023, the Ocean Policy Committee (OPC) requested that the ORAP advise on areas of opportunity for partnership (such as through the National Oceanic Partnership Program) on the topic of emerging technology (which could include Artificial Intelligence/<span class=\"match\">Machine Learning</span>, eDNA, and similar technology) with ocean industry and other sectors over the next 5-10 years. The OPC also requested that ORAP self-select another topic to address. The ORAP members agreed that the topic of accessible, inter-operable, interdisciplinary, and trusted"},{"title":"Increasing Market and Planning Efficiency Through Improved Software; Notice of Technical Conference: Increasing Market and Planning Efficiency Through Improved Software","type":"Notice","abstract":null,"document_number":"2026-04554","html_url":"https://www.federalregister.gov/documents/2026/03/09/2026-04554/increasing-market-and-planning-efficiency-through-improved-software-notice-of-technical-conference","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-03-09/pdf/2026-04554.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-04554.pdf?1772804713","publication_date":"2026-03-09","agencies":[{"raw_name":"DEPARTMENT OF ENERGY","name":"Energy Department","id":136,"url":"https://www.federalregister.gov/agencies/energy-department","json_url":"https://www.federalregister.gov/api/v1/agencies/136","parent_id":null,"slug":"energy-department"},{"raw_name":"Federal Energy Regulatory Commission","name":"Federal Energy Regulatory Commission","id":167,"url":"https://www.federalregister.gov/agencies/federal-energy-regulatory-commission","json_url":"https://www.federalregister.gov/api/v1/agencies/167","parent_id":136,"slug":"federal-energy-regulatory-commission"}],"excerpts":"address cutting edge research topics through individual presentations from industry in the same format as prior conferences in this series. Broadly, such topics fall into the following categories: \n (1) Software applications, including artificial intelligence (AI) or <span class=\"match\">machine learning</span>, to improve efficiency and affordability of the bulk power system, and implementation of advanced computing methods in electric and natural gas markets. \n (2) Software supporting the operation of energy infrastructure, including operations impacted by the interconnection"},{"title":"Agency Forms Undergoing Paperwork Reduction Act Review","type":"Notice","abstract":null,"document_number":"2026-04560","html_url":"https://www.federalregister.gov/documents/2026/03/09/2026-04560/agency-forms-undergoing-paperwork-reduction-act-review","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-03-09/pdf/2026-04560.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-04560.pdf?1772804713","publication_date":"2026-03-09","agencies":[{"raw_name":"DEPARTMENT OF HEALTH AND HUMAN SERVICES","name":"Health and Human Services Department","id":221,"url":"https://www.federalregister.gov/agencies/health-and-human-services-department","json_url":"https://www.federalregister.gov/api/v1/agencies/221","parent_id":null,"slug":"health-and-human-services-department"},{"raw_name":"Centers for Disease Control and Prevention","name":"Centers for Disease Control and Prevention","id":44,"url":"https://www.federalregister.gov/agencies/centers-for-disease-control-and-prevention","json_url":"https://www.federalregister.gov/api/v1/agencies/44","parent_id":221,"slug":"centers-for-disease-control-and-prevention"}],"excerpts":"analytics resulting in low positive predictive values for a number of conditions (which subsequently results in higher false positive and negative rates and downstream burden to families and the medical system). Smaller-scale work on the use of post-analytical tools such as <span class=\"match\">machine learning</span> algorithms have shown that incorporation of these elements into newborn screening can improve detection rates, while reducing false positives. These studies, however, have been limited to single sites and have not been integrated into the daily workflow of high-throughput"},{"title":"Notice of Availability of Software and Documentation for Licensing","type":"Notice","abstract":"Pursuant to the provisions of section 801 of Public Law 113-66 (2014 National Defense Authorization Act); the Department of the Air Force announces the availability of WIFI Distinct Native Attribute (DNA) Fingerprinting Demonstration Code, V23, dated 15 Nov 2023, to include source code (MATLAB m-files), experimentally collected WIFI data (MATLAB mat-files), and operation checking (Ops Check) documentation software and related documentation for to illustrate some basic elements of Distinct Native Attribute (DNA) fingerprinting. DNA fingerprints are extracted from radio frequency device emissions and used to discriminate (uniquely identify) specific hardware devices using machine learning (ML) techniques. The demonstrated discriminability is akin to using human fingerprints and/or human DNA to discriminate (identify) individuals. The package includes a series of folders and code for performing end-to-end DNA fingerprinting. The folders are sequentially numbered and include some experimentally collected WiFi signals; some 1D and 2D fingerprint extraction/ generation code, and some machine learning code for performing the discrimination. All of the code includes header information indicating contributing researchers and appropriate references for signals and systems where the DNA fingerprinting has been demonstrated.","document_number":"2023-28624","html_url":"https://www.federalregister.gov/documents/2023/12/28/2023-28624/notice-of-availability-of-software-and-documentation-for-licensing","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2023-12-28/pdf/2023-28624.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2023-28624.pdf?1703684729","publication_date":"2023-12-28","agencies":[{"raw_name":"DEPARTMENT OF DEFENSE","name":"Defense Department","id":103,"url":"https://www.federalregister.gov/agencies/defense-department","json_url":"https://www.federalregister.gov/api/v1/agencies/103","parent_id":null,"slug":"defense-department"},{"raw_name":"Department of the Air Force","name":"Air Force Department","id":13,"url":"https://www.federalregister.gov/agencies/air-force-department","json_url":"https://www.federalregister.gov/api/v1/agencies/13","parent_id":103,"slug":"air-force-department"}],"excerpts":"discriminate (uniquely identify) specific hardware devices using <span class=\"match\">machine learning</span> (ML) techniques. The demonstrated discriminability is akin to using human fingerprints and/or human DNA to discriminate (identify) individuals. The package includes a series of folders and code for performing end-to-end DNA fingerprinting. The folders are sequentially numbered and include some experimentally collected WiFi signals; some 1D and 2D fingerprint extraction/generation code, and some <span class=\"match\">machine learning</span> code for performing the discrimination. All of the code includes"},{"title":"Agency Information Collection Activities; Submission to the Office of Management and Budget (OMB) for Review and Approval; Comment Request; Commodity Flow Survey (CFS)","type":"Notice","abstract":"The Department of Commerce, in accordance with the Paperwork Reduction Act (PRA) of 1995, invites the general public and other Federal agencies to comment on proposed, and continuing information collections, which helps us assess the impact of our information collection requirements and minimize the public's reporting burden. The purpose of this notice is to allow for 60 days of public comment on the proposed reinstatement, with change, of the Commodity Flow Survey, prior to the submission of the information collection request (ICR) to OMB for approval.","document_number":"2026-10118","html_url":"https://www.federalregister.gov/documents/2026/05/20/2026-10118/agency-information-collection-activities-submission-to-the-office-of-management-and-budget-omb-for","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2026-05-20/pdf/2026-10118.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2026-10118.pdf?1779194716","publication_date":"2026-05-20","agencies":[{"raw_name":"DEPARTMENT OF COMMERCE","name":"Commerce Department","id":54,"url":"https://www.federalregister.gov/agencies/commerce-department","json_url":"https://www.federalregister.gov/api/v1/agencies/54","parent_id":null,"slug":"commerce-department"},{"raw_name":"Census Bureau","name":"Census Bureau","id":42,"url":"https://www.federalregister.gov/agencies/census-bureau","json_url":"https://www.federalregister.gov/api/v1/agencies/42","parent_id":54,"slug":"census-bureau"}],"excerpts":"that ship hazardous materials will decrease the number of establishments from 160,000 in 2022 to 140,000 in 2027; accepting estimates of shipping activity and allowing respondents the option to report their shipment weight in additional units of measurement; applying <span class=\"match\">machine learning</span> to code products based on their descriptions; and providing electronic reporting and including the option for consolidated reporting of multiple locations through a single login for larger companies. In conclusion, the above items will help reduce the burden for the"},{"title":"Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations; Draft Guidance for Industry and Food and Drug Administration Staff; Availability","type":"Notice","abstract":"The Food and Drug Administration (FDA or Agency) is announcing the availability of the draft guidance entitled \"Artificial Intelligence Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations.\" This draft guidance, when finalized, will provide recommendations regarding the contents of marketing submissions for devices that include artificial intelligence (AI)-enabled device software functions including documentation and information that will support FDA's evaluation of safety and effectiveness. To support the development of appropriate documentation for FDA's assessment of the device, this draft guidance also proposes recommendations for the design, development, and implementation of AI- enabled devices that sponsors may wish to consider using throughout the total product lifecycle (TPLC). This draft guidance is not final nor is it for implementation at this time.","document_number":"2024-31543","html_url":"https://www.federalregister.gov/documents/2025/01/07/2024-31543/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing","pdf_url":"https://www.govinfo.gov/content/pkg/FR-2025-01-07/pdf/2024-31543.pdf","public_inspection_pdf_url":"https://public-inspection.federalregister.gov/2024-31543.pdf?1736171123","publication_date":"2025-01-07","agencies":[{"raw_name":"DEPARTMENT OF HEALTH AND HUMAN SERVICES","name":"Health and Human Services Department","id":221,"url":"https://www.federalregister.gov/agencies/health-and-human-services-department","json_url":"https://www.federalregister.gov/api/v1/agencies/221","parent_id":null,"slug":"health-and-human-services-department"},{"raw_name":"Food and Drug Administration","name":"Food and Drug Administration","id":199,"url":"https://www.federalregister.gov/agencies/food-and-drug-administration","json_url":"https://www.federalregister.gov/api/v1/agencies/199","parent_id":221,"slug":"food-and-drug-administration"}],"excerpts":"oversight of medical devices, including AI-enabled devices, and has committed to developing guidances and resources for such an approach. Some recent efforts include developing guiding principles for good <span class=\"match\">machine learning</span> practice (GMLP) and transparency for <span class=\"match\">machine learning</span>-enabled devices to help promote safe, effective, and high-quality <span class=\"match\">machine learning</span> models; and a public workshop on fostering a patient-centered approach to AI-enabled devices, including discussion of device transparency for users. This draft guidance intends to continue these efforts"}]}