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

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

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.