Standards Database

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…

What is BS 10008-1 about? BS 10008-1 is the first part of the multi-series. BS 10008-1 specifies requirements for the implementation and operation of electronic information management systems, including the storage and transfer of electronically stored information (ESI). BS 10008-1…

1   Scope This part of BS 10102 provides guidance on good practice for implementing data-intensive projects to realize value, including: a) defining project objectives and project type; b) data project management methodology; c) project roles and responsibilities; d) defining the approach to…

This part of BS 10102 gives guidance on realizing value from data, including big data, such as gaining insights, informing strategies, enhancing reputation, and improving compliance, efficiency and performance. To achieve this, guidance is given on managing data efficiently, responsibly…

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…

This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key…

VDMA 15423 deals with the technical requirements for minimizing risks that may occur with newly placed on the market power-operated semi-automatic parking facilities for motor vehicles. The VDMA standard sheet does not apply to devices that are controlled fully automatically…

The NEMA 2018 IoTNOW webinar series was designed to learn about the IoT emerging trends that must be considered during the optimization of product performance and factories. The series covered the following twelve (12) topics: a. Augmented Reality (AR) b.…

Recommendation ITU-T M.3381 provides requirements for energy saving management of a 5G radio access network (RAN) system with artificial intelligence (AI). Recommendation ITU-T M.3381 explains the requirements of using AI technology to achieve energy saving management for communication units and…

This SAE Aerospace Information Report (AIR) discusses characteristics of data communications for the Joint Architecture for Unmanned Systems (JAUS). This document provides guidance on the aspects of transport media, unmanned systems and the characteristics of JAUS itself that are relevant…

Short description The scope of the expert recommendation is the design and layout of autonomous systems from the perspective of human reliability. In many technical areas, autonomous systems are being developed to support or replace humans in their tasks. The…

PD ISO/IEC TR 29794-5:2010 Information technology. Biometric sample quality. Face image data The purpose of this part of ISO/IEC 29794 is to define and specify methodologies for computation of objective, quantitative quality scores for facial images. Furthermore, the purpose, intent,…

Scope This document describes testing techniques (including those described in ISO/IEC/IEEE 29119-4) applicable for AI systems in the context of the AI system life cycle model stages defined in ISO/IEC 22989. It describes how AI and ML assessment metrics can…