ISO/IEC TR 5469:2024
PD ISO/IEC TR 5469:2024
International
ISO/IEC AWI TS 5471
Artificial intelligence — Quality evaluation guidelines for AI systems
This document provides guidelines for evaluation of AI systems using an AI system quality model. The document is applicable to…
ISO/IEC 24029-2:2023
Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 2: Methodology for the use of formal methods
ISO/IEC TR 24029-1:2021
Artificial Intelligence (AI) – Assessment of the robustness of neural networks – Part 1: Overview
This document provides background about existing methods to assess the robustness of neural networks. © ISO/IEC 2022 All rights reserved
ISO/IEC DIS 5259-1
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples
This document provides the landscape for understanding and associating of “Data quality for analytics and ML” series and guides the…
ISO/IEC FDIS 5259-2
ISO/IEC NP 5259-2
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures
This document provides a data quality model, data quality measures, and guidance on reporting data quality in the context of…
ISO/IEC FDIS 5259-3
ISO/IEC NP 5259-3
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines
This document specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving the quality for data used in…
ISO/IEC FDIS 5259-4
ISO/IEC 5259-4
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 4: Data quality process framework
This Recommendation describes the architecture, functional entities, and interfaces for a spontaneous dialogue processing system for language learning. © ITU…
ITU-T Y.3179
Architectural framework for machine learning model serving in future networks including IMT-2020
This Recommendation provides an architectural framework for machine learning (ML) models serving in future networks including IMT-2020, i.e., preparing and…
This Recommendation specifies an architectural framework for network automation based on artificial intelligence (AI) for resource and fault management in…
Recommendation ITU-T Y.3172 specifies an architectural framework for machine learning (ML) in future networks including IMT-2020. A set of architectural…
