Standards Database

This DIN SPEC in accordance with the PAS procedure has been drawn up by a DIN SPEC (PAS)-consortium set up on a temporary basis. This DIN SPEC (PAS) has been developed and approved by the authors named in the foreword.…

This DIN SPEC in accordance with the PAS procedure has been drawn up by a DIN SPEC (PAS)-consortium set up on a temporary basis. This DIN SPEC (PAS) has been developed and approved by the authors named in the foreword.…

This document specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving the quality for data used in the areas of analytics and machine learning. This document does not define a detailed process, methods or metrics. Rather it…

This document provides a data quality model, data quality measures, and guidance on reporting data quality in the context of analytics and machine learning (ML). This document builds on ISO 8000 series, ISO/IEC 25012 and ISO/IEC 25024. The aim of…

This document provides the landscape for understanding and associating of “Data quality for analytics and ML” series and guides the foundational concepts regarding data quality for analytics and artificial intelligence. It also describes associated technologies and examples (e.g. use cases…

This document gives a general model and interaction model based on the user interface of emotion computing, describing emotion representation, emotion data collection, emotion Sense recognition, emotional decision-making and emotional expression modules. Compiled and prepared by www.ChineseStandard.net. © British Standards…

This Recommendation describes the architecture, functional entities, and interfaces for a spontaneous dialogue processing system for language learning. © ITU 2022 All rights reserved

Recommendation ITU-T Y.3181 provides an architectural framework for machine learning (ML) sandbox in future networks including IMT-2020. More precisely, it describes requirements and high level architecture for ML sandbox in future networks including IMT-2020. © ITU 2023 All rights reserved

This Recommendation provides an architectural framework for machine learning (ML) models serving in future networks including IMT-2020, i.e., preparing and deploying ML models in different deployment environments to enable the application of ML model inference to ML underlay networks. The…

Recommendation ITU-T Y.3172 specifies an architectural framework for machine learning (ML) in future networks including IMT-2020. A set of architectural requirements and specific architectural components needed to satisfy these requirements are presented. These components include, but are not limited to,…

This Recommendation specifies an architectural framework for network automation based on artificial intelligence (AI) for resource and fault management in future networks, including international mobile telecommunications-2020 (IMT-2020). The purpose of the framework is to improve network efficiency and maintain quality…

What is BS AAMI 34971 – Application of ISO 14971 to machine learning in artificial intelligence – Guide about? Artificial intelligence can bring many benefits to healthcare but can also present unique risks. To treat those appropriately, BSI and the…

This standard describes specific methodologies to help users certify how they worked to address and eliminate issues of negative bias in the creation of their algorithms, where “negative bias” infers the usage of overly subjective or uniformed data sets or…

This document provides definitions, concepts, and guidelines to address specifically AI-enhanced nudging mechanisms by organisations.It focuses on a standard that aims to support existing legislations and allow industry to deal with AI-enhanced Nudging Mechanisms according to applicable standards, guidelines and…