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href="https://standards.ieee.org/initiatives/" title="Initiatives">Initiatives</a></li><li class="breadcrumb-item"><a href="https://standards.ieee.org/initiatives/autonomous-intelligence-systems/" title="Autonomous and Intelligent Systems (AIS)">Autonomous and Intelligent Systems (AIS)</a></li> <li class="breadcrumb-item active" aria-current="page">Autonomous and Intelligent Systems (AIS) Standards </li> </ol>  </nav>  </div>  </div>  </div>  </section>  <section id="content"> <div class="container"> <div class="row"> <div id="main-content" class="col-12 col-md-8 left"> <div class="post"><div class="wpb-content-wrapper"><div class="vc_row wpb_row vc_row-fluid"><div class="wpb_column vc_column_container vc_col-sm-12"><div class="vc_column-inner"><div class="wpb_wrapper"><h2 style="text-align: left" class="vc_custom_heading vc_do_custom_heading h3" >IEEE portfolio of AIS technology and impact standards and standards projects</h2> <div class="wpb_text_column wpb_content_element" > <div class="wpb_wrapper"> <p><a class="arrow-link" href="#p7000">View the IEEE 7000™ Standards & Projects</a></p> <p><a class="arrow-link" href="#2247">View the IEEE P2247™ Projects</a></p> </div> </div> <div class="vc_separator wpb_content_element vc_separator_align_center vc_sep_width_100 vc_sep_border_width_5 vc_sep_pos_align_center vc_separator_no_text vc_sep_color_grey wpb_content_element wpb_content_element mt-4 mb-4" ><span class="vc_sep_holder vc_sep_holder_l"><span class="vc_sep_line"></span></span><span class="vc_sep_holder vc_sep_holder_r"><span class="vc_sep_line"></span></span> </div><h3 style="text-align: left" class="vc_custom_heading vc_do_custom_heading h4 mt-0" >Projects & Standards</h3> <div class="wpb_text_column wpb_content_element" > <div class="wpb_wrapper"> <dl> <dt><a class="arrow-link" href="/ieee/1232/3896/">IEEE 1232™-2010 – IEEE Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)</a></dt> <dd class="ms-4">The purpose of AI-ESTATE is to standardize interfaces for functional elements of an intelligent diagnostic reasoner and representations of diagnostic knowledge and data for use by such diagnostic reasoners.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1232.3/4665/">IEEE 1232.3™-2014 – IEEE Guide for the Use of Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)</a></dt> <dd class="ms-4">Guidance to developers of IEEE Std 1232-conformant applications is provided in this guide.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1589/6073/">IEEE 1589™-2020 – IEEE Standard for Augmented Reality Learning Experience Model</a></dt> <dd class="ms-4">To support according implementations of AR training systems, this document proposes an overarching integrated conceptual model that describes interactions between the physical world, the user, and digital information, the context for AR-assisted learning and other parameters of the environment.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1636/6679/">IEEE 1636™-2018 – IEEE Standard for Software Interface for Maintenance Information Collection and Analysis (SIMICA)</a></dt> <dd class="ms-4">Promoting and facilitating interoperability between components of automatic test systems where test results and/or maintenance actions need to be shared is addressed in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1636.1/6947/">IEEE 1636.1™-2018 – IEEE Standard for Software Interface for Maintenance Information Collection and Analysis (SIMICA): Exchanging Test Results and Session Information via the eXtensible Markup Language (XML)</a></dt> <dd class="ms-4">Promoting and facilitating interoperability between components of automatic test systems where test results need to be shared is addressed in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1636.2/5766/">IEEE 1636.2™-2018 – IEEE Standard for Software Interface for Maintenance Information Collection and Analysis (SIMICA): Exchanging Maintenance Action Information via the Extensible Markup Language (XML)</a></dt> <dd class="ms-4">Promoting and facilitating interoperability components of automatic test systems where actions taken during maintenance need to be shared is addressed in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1855/5418/">IEEE 1855™-2016 – IEEE Standard for Fuzzy Markup Language</a></dt> <dd class="ms-4">A new specification language, named Fuzzy Markup Language (FML), is presented in this standard, exploiting the benefits offered by eXtensible Markup Language (XML) specifications and related tools in order to model a fuzzy logic system in a human-readable and hardware independent way.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1872/5354/">IEEE 1872™-2015 – IEEE Standard Ontologies for Robotics and Automation</a></dt> <dd class="ms-4">This standard is composed of a core ontology about R&A, called CORA, together with other ontologies that give support to CORA.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1872.2/7094/">IEEE 1872.2™-2021 – IEEE Standard for Autonomous Robotics (AuR) Ontology</a></dt> <dd class="ms-4">This standard extends IEEE Std 1872-2015, IEEE Standard for Ontologies for Robotics and Automation, to represent additional domain-specific concepts, definitions, and axioms commonly used in Autonomous Robotics (AuR).</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1873/5355/">IEEE 1873™-2015 – IEEE Standard for Robot Map Data Representation for Navigation</a></dt> <dd class="ms-4">A map data representation of environments of a mobile robot performing a navigation task is specified in this standard. It provides data models and data formats for two-dimensional (2D) metric and topological maps.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1876/5482/">IEEE 1876™-2019 – IEEE Standard for Networked Smart Learning Objects for Online Laboratories</a></dt> <dd class="ms-4">Methods for storing, retrieving, and accessing online laboratories as smart and interactive learning objects are defined in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1900.5.2/5618/">IEEE 1900.5.2™-2017 – IEEE Standard for Method for Modeling Spectrum Consumption</a></dt> <dd class="ms-4">A vendor-independent generalized method for modeling spectrum consumption of any type of use of radio frequency spectrum and the attendant computations for arbitrating the compatibility among models are defined in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/1948.1/11667/">IEEE P1948.1™ – Standard for Artificial Intelligence Based Network Applications in 5G and Beyond Mobile Networks</a></dt> <dd class="ms-4">The standard describes a unified framework for the integration of artificial intelligence (AI) based applications into 5G and 6G mobile communication networks.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2089/7633/">IEEE 2089™-2021 – IEEE Standard for an Age Appropriate Digital Services Framework Based on the 5Rights Principles for Children</a></dt> <dd class="ms-4">A set of processes by which organizations seek to make their services age appropriate is established in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2089.1/10700/">IEEE 2089.1™-2024 – IEEE Standard for Online Age Verification</a></dt> <dd class="ms-4">Framework for the design, specification, evaluation, and deployment of online age verification systems are established in this standard.</dd> <dt class="mt-4"><a id="2247" class="arrow-link" href="/ieee/2247.2/7522/">IEEE P2247.2™ – Interoperability Standards for Adaptive Instructional Systems (AISs)</a></dt> <dd class="ms-4">This standard defines interactions and exchanges among the components of adaptive instructional systems (AISs).</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2247.3/7523/">IEEE P2247.3™ – Recommended Practices for Evaluation of Adaptive Instructional Systems</a></dt> <dd class="ms-4">This recommended practice defines and classifies methods of evaluating adaptive instructional systems (AIS) and establishes guidance for the use of these methods. This best practice incorporates and promotes the principles of ethically aligned design for the use of artificial intelligence (AI) in AIS.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2410/7746/">IEEE 2410™-2021 – IEEE Standard for Biometric Privacy</a></dt> <dd class="ms-4">The Standard for Biometric Privacy (SBP) provides private identity assertion. SBP supersedes the prior IEEE Std 2410(TM)-2019 by including a formal specification for privacy and biometrics such that a conforming SBP system does not incur GDPR, CCPA, BIPA or HIPAA privacy obligations.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2660.1/6264/">IEEE 2660.1™-2020 – IEEE Recommended Practice for Industrial Agents: Integration of Software Agents and Low-Level Automation Functions</a></dt> <dd class="ms-4">The recommended practices to solve the interface problem when applying industrial agents, namely, integrating intelligent software agents with low-level automation devices in the context of cyber-physical systems, are described in this recommended practice.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2671/7176/">IEEE 2671™-2022 – IEEE Standard for General Requirements of Online Detection Based on Machine Vision in Intelligent Manufacturing</a></dt> <dd class="ms-4">This standard specifies through the general requirements of online detection based on machine vision, including requirements for data format, data transmission processes, definition of application scenarios and performance metrics for evaluating the effect of online detection deployment.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2672/7177/">IEEE 2672™-2023 – IEEE Guide for General Requirements of Mass Customization</a></dt> <dd class="ms-4">This guide provides the definitions, terminologies, operation procedures, system architectures, key technological requirements, data requirements and applications of and related to user-oriented mass customization.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2751/6994/">IEEE P2751™ – 3D Map Data Representation for Robotics and Automation</a></dt> <dd class="ms-4">This standard extends the IEEE 1873-2015 Standard for Robot Map Data Representation from two-dimensional (2D) maps to three-dimensional (3D) maps.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2755/6893">IEEE 2755™-2017 – IEEE Guide for Terms and Concepts in Intelligent Process Automation</a></dt> <dd class="ms-4">This standard is published for the purpose of promoting clarity and consistency in the use of Software Based Intelligent Process Automation (SBIPA) terminology.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2755.1/7518/">IEEE 2755.1™-2019 – IEEE Guide for Taxonomy for Intelligent Process Automation Product Features and Functionality</a></dt> <dd class="ms-4">This guide is published to create clarity for individuals involved with Software-Based Intelligent Process Automation products so that industry participants may rely on a product manufacturer’s functionality claims and understand the underlying technological methods used to produce those functions and how one might approach evaluating the relative sophistication and importance of each function or feature.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2755.2/7587/">IEEE 2755.2™-2020 – IEEE Recommended Practice for Implementation and Management Methodology for Software-Based Intelligent Process Automation</a></dt> <dd class="ms-4">Provided in this recommended practice is a comprehensive methodology for technology domain exploration, development of strategy, technology evaluation, implementation, management, operations, program optimization, and successful enterprise scaling for IPA programs while utilizing terminology as established in IEEE Std 2755™-2017 and technology taxonomy as established in IEEE Std 2755.1™-2019.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2801/7459/">IEEE 2801™-2022 – IEEE Recommended Practice for the Quality Management of Datasets for Medical Artificial Intelligence</a></dt> <dd class="ms-4">Promoted in this recommended practice are quality management activities for datasets used for artificial intelligence medical devices (AIMD).</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2802/7460/">IEEE 2802™-2022 – IEEE Standard for Performance and Safety Evaluation of Artificial Intelligence Based Medical Devices: Terminology</a></dt> <dd class="ms-4">This standard is aimed at establishing concepts and terminology for the performance and safety evaluation of artificial intelligence medical device, which covers basic technology, dataset, quality characteristics, quality evaluation and application scenario.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2807/7525/">IEEE 2807™-2022 – IEEE Standard for Framework of Knowledge Graphs</a></dt> <dd class="ms-4">A framework of knowledge graphs is proposed in this standard. This standard can be applied in various organizations that plan, design, develop, implement, and apply knowledge and in organizations that develop support technologies, tools, and services to knowledge graphs.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2807.1/7671/">IEEE 2807.1™-2024 – IEEE Approved Draft Standard for Technical Requirements and Evaluation of Knowledge Graphs</a></dt> <dd class="ms-4">This standard defines technical requirements, performance metrics, evaluation criteria and test cases for knowledge graphs.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2807.2/10140/">IEEE 2807.2™-2024 – IEEE Approved Draft Guide for Application of Knowledge Graphs for Financial Services</a></dt> <dd class="ms-4">This standard defines guidelines for application of knowledge graphs for financial services.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2807.4/10571/">IEEE P2807.4™ – Standard for Technical Requirements and Evaluation of Knowledge Graphs</a></dt> <dd class="ms-4">This guideline for Scientific Knowledge Graphs (SKG) specifies data scope, including the actors such as authors or organizations, the documents such as journal or conference publications, and the research knowledge such as research topics or technologies; SKG construction process, including knowledge acquisition, knowledge fusion, knowledge representation, or knowledge inference of scientific knowledge; and applications, including academic service, intelligence mining, or scholar analysis.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2813/7535/">IEEE 2813™-2020 – IEEE Standard for Big Data Business Security Risk Assessment</a></dt> <dd class="ms-4">This standard can be applied to internet-based business scenarios, and can also be served serve as a practical guide to achieve help assess business security risk control through the big data technology.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2817/7644/">IEEE P2817™ – Guide for Verification of Autonomous Systems</a></dt> <dd class="ms-4">This Guide for Verification of Autonomous Systems enables the user to define a customized process for verification of their autonomous system based on their available resources.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2830/10231/">IEEE 2830™-2021 – IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning</a></dt> <dd class="ms-4">The framework and architecture for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a trusted third party are defined in this standard. Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2840/7673/">IEEE P2840™ – IEEE Draft Standard for Responsible AI Licensing</a></dt> <dd class="ms-4">The IEEE P2840 standard pertains to licenses which, by subscribing to this standard, meaningfully engage with the spirit of responsible use of AI systems.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2841/7674/">IEEE 2841™-2022 – IEEE Recommended Practice for Framework and Process for Deep Learning Evaluation</a></dt> <dd class="ms-4">The recommendations on evaluating and improving algorithm reliability for shortening the development cycle of deep learning algorithms and improving the quality of software systems based on deep learning algorithms are defined in this document.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2842/7675/">IEEE 2842™-2021 – IEEE Recommended Practice for Secure Multi-Party Computation</a></dt> <dd class="ms-4">A technical framework for secure multi-party computation is provided in this standard, including specifying the following: an overview of secure multi-party computation; a technical framework of secure multi-party computation; security levels of secure multi-party computation; and use cases based on secure multi-party computation.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2846/10831/">IEEE 2846™-2022 – IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems</a></dt> <dd class="ms-4">This standard applies to road vehicles. It defines a minimum set of reasonable assumptions and foreseeable scenarios that shall be considered in the development of safety related models that are part of an automated driving system (ADS).</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2863/10142/">IEEE P2863™ – Recommended Practice for Organizational Governance of Artificial Intelligence</a></dt> <dd class="ms-4">This recommended practice specifies governance criteria such as safety, transparency, accountability, responsibility and minimizing bias, and process steps for effective implementation, performance auditing, training and compliance in the development or use of artificial intelligence within organizations.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2881/10248/">IEEE P2881™ – IEEE Draft Standard for Learning Metadata Terms</a></dt> <dd class="ms-4">This Standard describes a series of classes and properties, that intentionally align to a Resource Description Framework (RDF) type of model which can be represented in many formats, including graphs.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2894/11296/">IEEE 2894™-2024 – IEEE Approved Draft Guide for an Architectural Framework for Explainable Artificial Intelligence</a></dt> <dd class="ms-4">This guide provides a technological blueprint for building, deploying and managing machine learning models while meeting the requirements of transparent and trustworthy AI by adopting a variety of XAI methodologies.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2895/10319/">IEEE P2895™ – Standard Taxonomy for Responsible Trading of Human-Generated Data</a></dt> <dd class="ms-4">The standard defines a taxonomy, which shall be used to describe the rules and categories of data rights in data contracts that govern the capture, use, sharing and trade of data.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2933/7592/">IEEE 2933™-2024 – IEEE Approved Draft Standard for Clinical Internet of Things (IoT) Data and Device Interoperability with TIPPSS – Trust, Identity, Privacy, Protection, Safety, Security</a></dt> <dd class="ms-4">This standard establishes a framework with TIPPSS principles (Trust, Identity, Privacy, Protection, Safety, Security) for Clinical Internet of Things (IoT) data and device interoperability.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2937/10376/">IEEE 2937™-2022 – IEEE Standard for Performance Benchmarking for Artificial Intelligence Server Systems</a></dt> <dd class="ms-4">Formal methods for the performance benchmarking for AI server systems are provided in this standard, including approaches for test, metrics, and measure. In addition, the technical requirements for benchmarking tools are discussed.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2941/10363/">IEEE 2941™-2021 – IEEE Standard for Artificial Intelligence (AI) Model Representation, Compression, Distribution, and Management</a></dt> <dd class="ms-4">The AI development interface, AI model interoperable representation, coding format, and model encapsulated format for efficient AI model inference, storage, distribution, and management are discussed in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2941.1/10567/">IEEE 2941.1™-2022 – IEEE Standard for Operator Interfaces of Artificial Intelligence</a></dt> <dd class="ms-4">A set of operator interfaces frequently used in artificial intelligence (AI) applications is defined in this standard, where the AI operators refer to the standard building blocks and primitives for performing basic AI operations.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2941.2/10963/">IEEE 2941.2™-2023 – IEEE Standard for Application Programming Interfaces (APIs) for Deep Learning (DL) Inference Engines</a></dt> <dd class="ms-4">A set of application programming interfaces (APIs) that is aimed at breaking down the barriers between different deep learning (DL) inference engines and applications is defined in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2945/10472/">IEEE 2945™-2023 – IEEE Standard for Technical Requirements for Face Recognition</a></dt> <dd class="ms-4">A general technical architecture and technical requirements for face recognition systems are specified. Functional, performance, and security requirements are defined. This standard is applicable to the design and application of face recognition systems.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2951/10386/">IEEE P2951™ – Technical Requirements and Evaluation Methods for Intelligent Levels of Smart Home Devices</a></dt> <dd class="ms-4">This standard provides a general intelligence level architecture for smart home devices, in order to stipulate their capabilities equipped by various sensors, hardware and software.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2957/10381/">IEEE P2957™ – Standard for a Reference Architecture for Big Data Governance and Metadata Management</a></dt> <dd class="ms-4">This standard defines a big data governance, metadata management and machine-readable reference architecture to enable scalability, findability, accessibility, interoperability and reusability of datasets among corporate heterogeneous and cross-domain repositories.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2959/10372/">IEEE P2959™ – Standard for Technical Requirements of Standard-Oriented Knowledge Graphs</a></dt> <dd class="ms-4">This document specifies data and schema requirements for knowledge graphs constructed from published standards which can be automatically machine readable.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2975.1/10558/">IEEE P2975.1™ – Standard for Industrial Artificial Intelligence (AI) Data Attributes</a></dt> <dd class="ms-4">This standard defines attributes related to industrial Artificial Intelligence (AI) data that facilitates the classification, association, and mapping towards value creation using data.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2975.2/11185/">IEEE P2975.2™ – Standard for Model Verification & Validation of Industrial Artificial Intelligence Systems</a></dt> <dd class="ms-4">This standard defines verification and validation requirements and constraints to be satisfied by Artificial Intelligence Deep learning models developed and used for process efficiencies, process optimization and automation in the manufacturing industry.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2975.3/11186/">IEEE P2975.3™ – Recommended Practice for Software Framework for Industrial Artificial Intelligence (AI) At-the-edge</a></dt> <dd class="ms-4">This recommended practice describes a software framework for industrial AI at-the-edge, including key features, software building blocks for functionalities and interfaces.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2976/10522/">IEEE P2976™ – Standard for XAI – eXplainable Artificial Intelligence – for Achieving Clarity and Interoperability of AI Systems Design</a></dt> <dd class="ms-4">This standard defines mandatory and optional requirements and constraints that need to be satisfied for an AI method, algorithm, application or system to be recognized as explainable.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/2986/10564/">IEEE 2986™-2023 – IEEE Recommended Practice for Privacy and Security for Federated Machine Learning</a></dt> <dd class="ms-4">This document provides recommended practices related to privacy and security for Federated Machine Learning, including security and privacy principles, defense mechanisms against non-malicious failures and examples of adversarial attacks on a Federated Machine Learning system.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3079.3.1/11021/">IEEE P3079.3.1™ – Standard for Service Application Programming Interfaces (APIs) for Digital Human Authoring and Visualization</a></dt> <dd class="ms-4">This standard specifies service APIs and a framework that includes required components, metadata, and data for digital human authoring and realistic visualization.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3079.3/10789/">IEEE 3079.3™-2023 – IEEE Standard for a Framework for Evaluating the Quality of Digital Humans</a></dt> <dd class="ms-4">A framework for the evaluation of the quality of digital humans is provided.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3109/11165/">IEEE P3109™ – Standard for Arithmetic Formats for Machine Learning</a></dt> <dd class="ms-4">This standard defines a binary arithmetic and data format for machine learning-optimized domains.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3110/11253/">IEEE P3110™ – Standard for Computer Vision (CV) – Technical Requirements for Algorithms Application Programming Interfaces (APIs) of Deep Learning Framework</a></dt> <dd class="ms-4">This standard establishes the application programming interfaces (API) model of the computer vision systems.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3119/10729/">IEEE P3119™ – Standard for the Procurement of Artificial Intelligence and Automated Decision Systems</a></dt> <dd class="ms-4">This standard establishes a uniform set of definitions and a process model for the procurement of Artificial Intelligence (AI) and Automated Decision Systems (ADS) by which government entities can address socio-technical and responsible innovation considerations to serve the public interest.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3123/10744/">IEEE P3123™ – Standard for Artificial Intelligence and Machine Learning (AI/ML) Terminology and Data Formats</a></dt> <dd class="ms-4">The standard defines specific terminology utilized in artificial intelligence and machine learning (AI/ML).</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3127/10745/">IEEE P3127™ – Guide for an Architectural Framework for Blockchain-based Federated Machine Learning</a></dt> <dd class="ms-4">This guide specifies an architectural framework and application guidelines for Blockchain based Federated Machine Learning.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3128/10746/">IEEE P3128™ – IEEE Draft Recommended Practice for The Evaluation of Artificial Intelligence (AI) Dialogue System Capabilities</a></dt> <dd class="ms-4">An evaluation framework for the intelligent capabilities of artificial intelligence (AI) dialogue systems such as chatbots, consulting terminals, or operation interfaces is established in this recommended practice.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3129/10747/">IEEE 3129™-2023 – IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-based Image Recognition Service</a></dt> <dd class="ms-4">Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3333.1.3/7136/">IEEE 3333.1.3™-2022 – IEEE Standard for the Deep Learning-Based Assessment of Visual Experience Based on Human Factors</a></dt> <dd class="ms-4">This standard defines deep learning-based metrics of content analysis and quality of experience (QoE) assessment for visual contents, which is an extension of Standard for the Quality of Experience (QoE) and Visual-Comfort Assessments of Three-Dimensional (3D) Contents Based on Psychophysical Studies (IEEE 3333.1.1™) and Standard for the Perceptual Quality Assessment of Three Dimensional (3D) and Ultra High Definition (UHD) Contents (IEEE 3333.1.2™).</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3142/10832/">IEEE P3142™ – Recommended Practice on Distributed Training and Inference for Large-scale Deep Learning Models</a></dt> <dd class="ms-4">This recommended practice specifies principles, approaches, and key performance indicators for distributed training and inference of large-scale deep learning models.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3152/11297/">IEEE P3152™ – IEEE Draft Standard for Transparent Agency Identification of Humans and Machines</a></dt> <dd class="ms-4">The purpose of this standard is to describe audio and visual marks to assist with the identification of the agency behind media or communications, such as a machine intelligence, a human being, or a combination of entities.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3154/10842/">IEEE 3154™-2024 – IEEE Recommended Practice for the Application of Knowledge Graphs for Talent Services</a></dt> <dd class="ms-4">This recommended practice assists developers in constructing knowledge graphs in the field of talent services more efficiently and consistently.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3156/10834/">IEEE 3156™-2023 – IEEE Standard for Requirements of Privacy-Preserving Computation Integrated Platforms</a></dt> <dd class="ms-4">Requirements of privacy computation integrated platforms, including the reference architecture, the functional requirements, the performance requirements, and the security requirements of privacy-preserving computation integrated platforms are provided by this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3157/10876/">IEEE P3157™ – Recommended Practice for Vulnerability Test for Machine Learning Models for Computer Vision Applications</a></dt> <dd class="ms-4">This recommended practice provides a framework for vulnerability tests for machine learning models in the computer vision domain.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3168/10923/">IEEE 3168™-2024 – IEEE Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service That Uses Machine Learning</a></dt> <dd class="ms-4">The natural language processing (NLP) services using machine learning have rich applications in solving various tasks and have been widely deployed and used, usually accessible by application programming interface (API) calls.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3181/10958/">IEEE P3181™ – Standard for Trusted Environment Based Cryptographic Computing</a></dt> <dd class="ms-4">This standard defines trusted environment based cryptographic computing systems including participants and computation nodes.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3187/10959/">IEEE P3187™ – IEEE Draft Guide for Framework for Trustworthy Federated Machine Learning</a></dt> <dd class="ms-4">In this standard, a general view on framework for trustworthy federated machine learning is provided in four parts: a principle in trustworthy federated machine learning, requirements from the perspective of different principles and different federated machine learning participants, and methods to realize trustworthy federated machine learning.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3198/11068/">IEEE P3198™ – Standard for Evaluation Method of Machine Learning Fairness</a></dt> <dd class="ms-4">This standard specifies a method for evaluating the fairness of machine learning.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3205/10237/">IEEE 3205™-2023 – IEEE Standard for Blockchain Interoperability Data Authentication and Communication Protocol</a></dt> <dd class="ms-4">Provided in this standard are an infrastructure of cross-chain interoperability, as well as interfaces and protocols of data authentication and communication for homogeneous and heterogeneous blockchain interoperability.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3300/11005/">IEEE 3300™-2022 – IEEE Standard Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Multimodal Conversion Version 1.2</a></dt> <dd class="ms-4">This standard adopts MPAI Technical Specification Version 1.2 as an IEEE Standard. Multimodal Conversation (MPAI-MMC) is an MPAI Standard comprising five use cases, all sharing the use of artificial intelligence (AI) to enable a form of human-machine conversation in completeness and intensity.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3301/11096/">IEEE 3301™-2022 – IEEE Standard Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Artificial Intelligence Framework (AIF) 1.1</a></dt> <dd class="ms-4">This standard adopts MPAI AI Framework (MPAI-AIF) Technical Specification Version 1.1 as an IEEE Standard. The MPAI-AIF Technical Specification specifies architecture, interfaces, protocols, and Application Programming Interfaces (API) of an AI Framework (AIF), especially designed for the execution of AI-based implementation, but also suitable for mixed AI and traditional data processing workflow.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3302/11006/">IEEE 3302™-2022 – IEEE Standard Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Context-based Audio Enhanced (CAE) Version 1.4</a></dt> <dd class="ms-4">This standard adopts MPAI Technical Specification Version 1.4 as an IEEE Standard. The Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Context-based Audio Enhancement (CAE) Version 1.4 is a collection of four use cases specifying AI-based technologies for audio-related applications including entertainment, communication, post-production, teleconferencing, and restoration.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3303/11134/">IEEE 3303™-2023 – IEEE Standard Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Compression and Understanding of Industrial Data 1.1</a></dt> <dd class="ms-4">This standard adopts MPAI Technical Specification Version 1.1 as an IEEE Standard. The Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Compression and Understanding of Industrial Data (MPI-CUI) Version 1.1predicts the performance of a Company from its Governance, Financial and Risk data in a Prediction Horizon expression as Default Probability, adequacy Index of Organizational Model, and Business Continuity Index.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3304/11281/">IEEE 3304™-2023 – IEEE Standard for Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Neural Network Watermarking (NNW) V1</a></dt> <dd class="ms-4">This is an adoption of the Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI)—Technical Specification Neural Network Watermarking as an IEEE Standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3307/11503/">IEEE P3307™ – Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Connected Autonomous Vehicle – Architecture (CAV) Version 1</a></dt> <dd class="ms-4">Technical Specification: Connected Autonomous Vehicle (MPAI-CAV) – Architecture (in the following also called MPAI-CAV – Architecture) specifies the Architecture of a Connected Autonomous Vehicle (CAV).</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3333.1.3/7136/">IEEE 3333.1.3™-2022 – IEEE Standard for the Deep Learning-Based Assessment of Visual Experience Based on Human Factors</a></dt> <dd class="ms-4">In this standard, QoE assessment is categorized into two subcategories which are perceptual quality and virtual reality (VR) cybersickness.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3394/11377/">IEEE P3394™ – Standard for Large Language Model Agent Interface</a></dt> <dd class="ms-4">This standard defines natural language interfaces that facilitate communication between Large Language Model (LLM) applications, agents, and human users.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3404/11391/">IEEE P3404™ – Standard for Requirements and Framework for Sharing Data and Models for Artificial Intelligence across Multiple Computing Centers</a></dt> <dd class="ms-4">This standard specifies the general requirements and framework of data and model sharing across multiple computing centers, including the requirements on functional architecture, interfaces, security, and performance.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3419/11384/">IEEE P3419™ – Standard for Large Language Model Evaluation</a></dt> <dd class="ms-4">This standard establishes a comprehensive set of criteria for the evaluation of Large Language Models (LLMs) and extends to multimodal models.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3428/11489/">IEEE P3428™ – Standard for Large Language Model Agents for AI-powered Education</a></dt> <dd class="ms-4">This standard provides Agent Components, Agent Interoperability Protocols, Agent Life Cycle and States, Foundation Models and LLM Embedding Mechanisms, and Evaluation of Education LLM Agents.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3447/11519/">IEEE P3447™ – System Architecture and General Requirements for Pre-trained Large Language Models (LLM) Applications in the Smart Home Industry</a></dt> <dd class="ms-4">This standard provides definitions, terms, frameworks, and general requirements for systems that apply pre-trained large language models (LLM) in the smart home industry.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3458/11520/">IEEE P3458™ – Standard for Domain-Specific Large Language Model Management Platforms</a></dt> <dd class="ms-4">This standard specifies a domain-specific architecture for Large Language Models (LLMs) Management Platforms that includes: functional, service, and deployment requirements.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3462/11584/">IEEE P3462™ – Recommended Practice for Using Safety by Design in Generative Models to Prioritize Child Safety</a></dt> <dd class="ms-4">This recommended practice follows a safety by design approach by providing recommendations for developing, deploying, and maintaining generative artificial intelligence models with adequate safeguards against child sexual abuse.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3652.1/7453/">IEEE 3652.1™-2020 – IEEE Guide for Architectural Framework and Application of Federated Machine Learning</a></dt> <dd class="ms-4">A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3800/10298/">IEEE P3800™ – IEEE Draft Standard for a data-trading system: overview, terminology and reference model</a></dt> <dd class="ms-4">In this document, a standard is defined for setting up and operationalizing a data trading system (DTS) to trade data through a domain-independent and principled marketplace under a unified architecture.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/3803/10301/">IEEE 3803™-2024 – IEEE Approved Draft Standard for Household Appliance Customer Data Assetization and Commercialization Requirements</a></dt> <dd class="ms-4">This standard governs the use, management, and privacy of customer data from household appliances during data assetization.</dd> <dt class="mt-4"><a id="p7000" class="arrow-link" href="/ieee/7000/6781/">IEEE 7000™-2021 – IEEE Standard Model Process for Addressing Ethical Concerns during System Design</a></dt> <dd class="ms-4">A set of processes by which organizations can include consideration of ethical values throughout the stages of concept exploration and development is established by this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7001/6929/">IEEE 7001™-2021 – IEEE Standard for Transparency of Autonomous Systems</a></dt> <dd class="ms-4">Measurable, testable levels of transparency, so that autonomous systems can be objectively assessed, and levels of compliance determined, are described in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7002/6898/">IEEE 7002™-2022 – IEEE Standard for Data Privacy Process</a></dt> <dd class="ms-4">The requirements for a systems/software engineering process for privacy-oriented considerations regarding products, services, and systems utilizing employee, customer, or other external user’s personal data are defined by this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7003/11357/">IEEE P7003™ – IEEE Draft Standard for Algorithmic Bias Considerations</a></dt> <dd class="ms-4">This draft standard describes processes and methodologies to help users address issues of bias in the creation of algorithms.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7004/10270/">IEEE P7004™ – Standard for Child and Student Data Governance</a></dt> <dd class="ms-4">This standard provides stakeholders with certifiable and responsible child and student data governance methodologies.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7004.1/10285/">IEEE P7004.1™ – Recommended Practices for Virtual Classroom Security, Privacy and Data Governance</a></dt> <dd class="ms-4">This recommended practice produces best practices for meeting the requirements of IEEE P7004: Standard for Child and Student Data Governance when designing, provisioning, configuring, operating, and maintaining an online virtual classroom experience for synchronous online learning, education, and training.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7005/7014/">IEEE 7005™-2021 – IEEE Standard for Transparent Employer Data Governance</a></dt> <dd class="ms-4">Specific methodologies to help employers in accessing, collecting, storing, utilizing, sharing, and destroying employee data are described in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7007/7070/">IEEE 7007™-2021 – IEEE Ontological Standard for Ethically Driven Robotics and Automation Systems</a></dt> <dd class="ms-4">A set of ontologies with different abstraction levels that contain concepts, definitions, axioms, and use cases that assist in the development of ethically driven methodologies for the design of robots and automation systems is established by this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7008/7095/">IEEE P7008™ – Standard for Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems</a></dt> <dd class="ms-4">“Nudges” as exhibited by robotic, intelligent or autonomous systems are defined as overt or hidden suggestions or manipulations designed to influence the behavior or emotions of a user. This standard establishes a delineation of typical nudges (currently in use or that could be created). It contains concepts, functions and benefits necessary to establish and ensure ethically driven methodologies for the design of the robotic, intelligent and autonomous systems that incorporate them.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7009/7096/">IEEE 7009™-2024 – IEEE Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems</a></dt> <dd class="ms-4">A practical, technical baseline of specific methodologies and tools for the development, implementation, and use of effective fail-safe mechanisms in autonomous and semi-autonomous systems is established in this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7010/7718/">IEEE 7010™-2020 – IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being</a></dt> <dd class="ms-4">The impact of artificial intelligence or autonomous and intelligent systems (A/IS) on humans is measured by this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7010.1/10756/">IEEE P7010.1™ – Recommended Practice for Environmental Social Governance (ESG) and Social Development Goal (SDG) Action Implementation and Advancing Corporate Social Responsibility</a></dt> <dd class="ms-4">IEEE Standards Project to provide recommendations for next steps in the application of IEEE Std 7010, applied to meeting Environmental Social Governance (ESG) and Social Development Goal (SDG) initiatives and targets.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7011/7191/">IEEE P7011™ – Standard for the Process of Identifying and Rating the Trustworthiness of News Sources</a></dt> <dd class="ms-4">This standard provides semi-autonomous processes using standards to create and maintain news purveyor ratings for purposes of public awareness.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7012/7192/">IEEE P7012™ – Standard for Machine Readable Personal Privacy Terms</a></dt> <dd class="ms-4">The standard identifies/addresses the manner in which personal privacy terms are proffered and how they can be read and agreed to by machines.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7014/7648/">IEEE 7014™-2024 – IEEE Standard for Ethical Considerations in Emulated Empathy in Autonomous and Intelligent Systems</a></dt> <dd class="ms-4">Guidance and actions for the ethical development, deployment, or decommission of autonomous and intelligent systems that attempt to emulate aspects of human empathy are provided by this standard.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7015/10688/">IEEE P7015™ – Standard for Data and Artificial Intelligence (AI) Literacy, Skills, and Readiness</a></dt> <dd class="ms-4">To coordinate global data and AI literacy building efforts, this standard establishes an operational framework and associated capabilities for designing policy interventions, tracking their progress, and empirically evaluating their outcomes.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7016/11178/">IEEE P7016™ – Standard for Ethically Aligned Design and Operation of Metaverse Systems</a></dt> <dd class="ms-4">This standard defines a methodology for creating possible Metaverse systems.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7016.1/11237/">IEEE P7016.1™ – Standard for Ethically Aligned Educational Metadata in Extended Reality (XR) & Metaverse</a></dt> <dd class="ms-4">This standard defines a high-level overview of a conceptual data schema for a metadata instance based on ethics concepts for a learning object utilized within XR systems and Metaverse applications.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7017/11133/">IEEE P7017™ – Recommended Practice for Design-Centered Human-Robot Interaction (HRI) and Governance</a></dt> <dd class="ms-4">This recommended practice describes the methodology and application of ‘compliance by design’ in the area of human-robot interaction (HRI) with regard to socially assistive robots.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7018/11306/">IEEE P7018™ – Standard for Security and Trustworthiness Requirements in Generative Pretrained Artificial Intelligence (AI) Models</a></dt> <dd class="ms-4">This standard establishes a comprehensive framework for mitigating security risks, privacy leaking in the development, deployment, and use of generative pretrained AI models.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/7100/11671/">IEEE P7100™ – Standard for Measurement of Environmental Impacts of Artificial Intelligence Systems</a></dt> <dd class="ms-4">This standard defines a measurement framework for reporting on environmental indicators for training models and deriving inference on Artificial Intelligence (AI) systems.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/8000/11235/">IEEE P8000™ – Standard for Characterization and Specification of Ethical Properties in Autonomous Intelligent Systems (AIS)</a></dt> <dd class="ms-4">The standard provides a framework for the characterization and specification of a suite of desirable and beneficial ethical properties and criteria of Autonomous Intelligent Systems (AIS) throughout their life-cycle, as well as negative properties to avoid.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/11073-10101/10343/">IEEE/ISO 11073-10101™-2020 – ISO/IEEE International Standard – Health informatics-Device interoperability-Part 10101: Point-of-care medical device communication-Nomenclature</a></dt> <dd class="ms-4">Within the context of the ISO/IEEE 11073 family of standards for point-of-care (POC) and personal health devices (PHD) medical device communication (MDC), this standard provides the nomenclature that supports both the domain information model and service model components of the standards family, as well as the semantic content exchanged with medical devices.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/24765/6800/">IEEE/ISO/IEC 24765™-2017 – ISO/IEC/IEEE International Standard – Systems and software engineering–Vocabulary</a></dt> <dd class="ms-4">This document provides a common vocabulary applicable to all systems and software engineering work.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/24774/10126/">IEEE/ISO/IEC 24774™-2021 – ISO/IEC/IEEE International Standard -Systems and software engineering – Life cycle management–Specification for process description</a></dt> <dd class="ms-4">This document presents requirements for the description of processes in terms of their format, content and level of prescription.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/62270/5770/">IEEE/IEC 62270™-2013 – IEC/IEEE Guide for Computer-based Control for Hydroelectric Power Plant Automation</a></dt> <dd class="ms-4">This guide addresses the application, design concepts, and implementation of computer-based control systems for hydroelectric plant automation.</dd> <dt class="mt-4"><a class="arrow-link" href="/ieee/62526/4252/">IEEE/IEC 62526™-2007 – Standard for Extensions to Standard Test Interface Language (STIL) for Semiconductor Design Environments</a></dt> <dd class="ms-4">Standard Test Interface Language (STIL) provides an interface between digital test generation tools and test equipment.</dd> </dl> </div> </div> </div></div></div></div> </div></div> </div>  <div id="sidebar" class="col-12 col-md-4 right"> <div class="widget"> <ul id="nav-secondary" class="nav flex-column "><li class="nav-item"><a class="nav-link " title="Applied Case Studies for Designing Trustworthy Digital Experiences for Children" href="https://standards.ieee.org/initiatives/autonomous-intelligence-systems/report-2/">Applied Case Studies for Designing Trustworthy Digital Experiences for Children </a></li><li class="nav-item"><a class="nav-link " title="Applied Children’s Case Studies" href="https://standards.ieee.org/initiatives/autonomous-intelligence-systems/childrens-data-governance/">Applied Children’s Case Studies </a></li><li class="nav-item"><a 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