Standards Database

This British Standard specifies requirements for the development of safe, effective, and ethical healthcare AI products. This British Standard specifies requirements for evaluation criteria, including: clinical benefits; standards of performance;successful and safe integration into the clinical work environment; ethical considerations;…

This recommended practice specifies the general requirements of three dimensional (3D) object reconstruction based on deep learning, including terminology, requirements for data format, functional processes, application scenarios and evaluating, metrics for evaluating the processes and effect of 3D object reconstruction.…

This document is a guide and record of the development for the NIST (National Institute of Standards and Technology) glossary of terms for trustworthy and responsible artificial intelligence (AI) and machine learning (ML). The glossary effort seeks to promote a…

This standard provides definitions, terms, frameworks, and general requirements for systems that apply pre-trained large language models (LLM) in the smart home industry. It covers foundational platforms, model capabilities, scenario applications, and safety requirements. © Copyright 2024 IEEE – All…

This document describes the principles and framework for environmental impact measurement of artificial intelligence systems and services and provides guidelines for impact reduction throughout the lifecycle. It includes a framework for defining the environmental impact of artificial intelligence, aharmonized calculation…

1.1 This practice considers impairments of communications within an automatic, automated, or autonomous unmanned ground vehicle (A­UGV) system during task execution. An A-UGV system typically uses communications between an A-UGV and fixed system components and resources, such as off-board control,…

The purpose of this standard is to define elements to improve transparency in the identification of the agency behind media or communications, such as a machine intelligence, a human being, or a combination of entities. The goal is to facilitate…

This standard addresses Artificial Intelligence (AI) risks such as malicious use, AI race, organizational risks, and rogue AI’s. The standard defines the relevant terminology, potential applications, and a risk abatement strategy for the behavior of AI models. The standard does…

This standard establishes a comprehensive framework for mitigating security risks, privacy leaking in the development, deployment, and use of generative pretrained AI models. The framework encompasses requirements concerning security risks, functional and non-functional requirements, and evaluation metrics such as transparency,…

This standard establishes a comprehensive set of criteria for the evaluation of Large Language Models (LLMs) and extends to multimodal models. Primarily, the standard provides a multi-dimensional evaluation framework for the users, teams, and enterprises who evaluate or use LLM…

This standard provides: 1. Agent Components: The building blocks and architectural elements that constitute an educational Large Language Model (LLM) agent. 2. Agent Interoperability Protocols: A communication protocol for interaction between different LLM agents and other components in the educational…

This standard outlines the toolchain for deploying artificial intelligence (AI) models on edge devices and specifies the functional requirements for this process. The standard covers key areas such as frontend adaptation, model compression, graph optimization, backend adaptation, compiling optimization, and…

This standard applies to the design, development, application, and evaluation of large-scale deep learning models for artificial intelligence. The standard specifies a technical reference architecture of large scale deep learning models. Furthermore, the standard provides general requirements on basic software…

This standard defines a comprehensive framework for federated machine learning of semantic information agents. It targets two primary layers: • Information Protocol Layer: This layer focuses on binary compression and protocol-level data structures for federated information exchange, with an emphasis…

This document specifies a method for evaluating the fairness of machine learning. Multiple causes contribute to the unfairness of machine learning. In this document, these causes of machine learning unfairness are categorized. The widely recognized and used definitions of machine…