IEEE P3333.1.3 - Standard for the Deep Learning Based Assessment of Visual Experience Based on Human Factors
IEEE P3333.1.3
https://standards.ieee.org/project/3333_1_3.html
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ISO/IEC TR 20547-5:2018 - Information technology– Big data reference architecture– Part5: Standards roadmap
ISO/IEC TR 20547-5:2018 describes big data relevant standards, both in existence and under development, along with priorities for future big data standards development based on gap analysis.
ISO/IEC TR 20547-5
https://www.iso.org/standard/72826.html
Information technology– Big data reference architecture– Part2: Use cases and derived requirements
SO/IEC TR 20547-2:2018 provides examples of big data use cases with application domains and technical considerations derived from the contributed use cases.
ISO/IEC TR 20547-2
https://www.iso.org/standard/71276.html
Information technology– Big data reference architecture– Part1: Framework and appli-cation process
This document describes the framework of the big data reference architecture and the process for how a user of the document can apply it to their particular problem domain.
ISO/IEC TR 20547-1:2020
https://www.iso.org/standard/71275.html
Information technology — Artificial Intelligence — Assessment of machine learning classification performance
Assessment of AI systems: Metrics for the performance capability of AI
ISO/IEC WD TS 4213
https://www.iso.org/standard/79799.html
Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI-based systems
Software quality: Quality assessment for AI-based systems (see also 4.1.1 and 4.3.1.4)
ISO/IECNP 5059
https://www.iso.org/standard/80655.html
Guideline for the develop-ment of deep learning image recognition systems
Procedure for data collection, structuring of data for learn-ing AI image recognition, process structure of learning experiments and quality assurance
DIN SPEC 13266
https://www.dke.de/resource/blob/2017010/99bc6d952073ca88f52c0ae4a8c351a8/nr-ki-english---download-data.pdf
Artificial Intelligence– Life Cycle Processes and Quality Requirements– Part2: Robustness
AI-specific requirements with regard to robustness, espe-cially regarding adversarial robustness and corruption robustness
DIN SPEC 92001-2
https://www.dke.de/resource/blob/2017010/99bc6d952073ca88f52c0ae4a8c351a8/nr-ki-english---download-data.pdf
HL7 Version 3 Standard: Refinement, Constraint and Localization to Version 3 Messages, Release 2
This HL7 Version 3 (V3) standard describes the processes whereby HL7 V3 artifact specifications may be refined, constrained and extended to support implementation designs, conformance profiles, and realm-specific standards.
ANSI/HL7 V3 RCL, R2-2007 (R2020)
ISO/TR 9241-810 - Ergonomics of human-system interaction - Part 810: Robotic, intelligent and autonomous systems
ISO/TR 9241-810
https://www.iso.org/standard/76577.html
M.resm-AI - Requirements for energy saving management of 5G RAN system with AI
M.resm-AI
https://www.itu.int/itu-t/workprog/wp_item.aspx?isn=16435
M.AI-TOM - Framework of AI enhanced Telecom Operation and Management (AITOM)
M.AI-TOM
https://www.itu.int/itu-t/workprog/wp_item.aspx?isn=16432