Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 3: Data quality management requirements and guidelines

CSA Group
Standards Development Organisation:
Canadian Standards Association (operating as CSA Group)
Working Program:
View the CSA work program
Designation Number:
ISO/IEC 5259-3
Standard Type:
National Standard of Canada - Adoption of International Standard
Standard Development Activity:
New Standard
ICS code(s):
35.020
Status:
Proceeding to development
SDO Comment Period Start Date:
SDO Comment Period End Date:
Posted On:

Scope:

This document specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving the quality of data used in the areas of analytics and machine learning.

This document does not define a detailed process, methods or metrics. Rather it defines the requirements and guidance for a quality management process along with a reference process and methods that can be tailored to meet the requirements in this document.
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The requirements and recommendations set out in this document are generic and are intended to be applicable to all organizations, regardless of type, size or nature.

Project need:

To align Canadian requirements with those of international standards in the Artificial Intelligence (AI) subject area. Additionally, the standards will provide support for existing and upcoming regulations around AI in Canada. This proposed New Standard is being adopted at the request of Canadian Stakeholders from P123 Technical Committee on Information Technology (TCIT). It will provide the industry with the latest requirements in the field of Artificial Intelligence (AI)

Note: The information provided above was obtained by the Standards Council of Canada (SCC) and is provided as part of a centralized, transparent notification system for new standards development. The system allows SCC-accredited Standards Development Organizations (SDOs), and members of the public, to be informed of new work in Canadian standards development, and allows SCC-accredited SDOs to identify and resolve potential duplication of standards and effort.

Individual SDOs are responsible for the content and accuracy of the information presented here. The text is presented in the language in which it was provided to SCC.