CEN/CLC/JTC 21/WG 3 - Engineering aspects

This document describes how to address unwanted bias in AI systems that use machine learning to conduct classification and regression tasks. This document provides mitigation techniques that can be applied throughout the AI system life cycle in order to treat unwanted bias. This document is applicable to all types and sizes of organization.

This document provides an overview on AI-related standards, with a focus on data and data life cycles, to organizations, agencies, enterprises, developers, universities, researchers, focus groups, users, and other stakeholders that are experiencing this era of digital transformation.
It describes links among the many international standards and regulations published or under development, with the aim of promoting a common language, a greater culture of quality, giving an information framework.
It addresses the following areas:
-   data governance;
-   data quality;
-   elements for data, data sets properties to provide unbiased evaluation and information for testing.

This document defines the stages and identifies associated actions for data processing throughout the
artificial intelligence (AI) system life cycle, including acquisition, creation, development, deployment,
maintenance and decommissioning. This document does not define specific services, platforms or tools.
This document is applicable to all organizations, regardless of type, size or nature, that use data in the
development and use of AI systems.

This document provides background about existing methods to assess the robustness of neural networks.

This document specifies a data quality model, data quality measures and guidance on reporting data quality in the context of analytics and machine learning (ML).
This document is applicable to all types of organizations who want to achieve their data quality objectives.

This document provides the means for understanding and associating the individual documents of the ISO/IEC “Artificial intelligence — Data quality for analytics and ML” series and is the foundation for conceptual understanding of data quality for analytics and machine learning. It also discusses associated technologies and examples (e.g. use cases and usage scenarios

This document establishes general common organizational approaches, regardless of the type, size or nature of the applying organization, to ensure data quality for training and evaluation in analytics and machine learning (ML). It includes guidance on the data quality process for:
—   supervised ML with regard to the labelling of data used for training ML systems, including common organizational approaches for training data labelling;
—   unsupervised ML;
—   semi-supervised ML;
—   reinforcement learning;
—   analytics.
This document is applicable to training and evaluation data that come from different sources, including data acquisition and data composition, data preparation, data labelling, evaluation and data use. This document does not define specific services, platforms or tools.