Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

ITU-T Y.3654 - Big data driven networking - Machine learning mechanism



Recommendation ITU-T Y.3654 specifies the mechanisms of machine learning in big data driven networking (bDDN). A set of related aspects of machine learning in bDDN are presented, these aspects include: overview, learning procedure, deployment, interfaces, learning path and control path, security considerations. Copyright © ITU 2008 All Rights Reserved

The information about this standard has been compiled by the AI Standards Hub, an initiative dedicated to knowledge sharing, capacity building, research, and international collaboration in the field of AI standards. You can find more information and interactive community features related to this standard by visiting the Hub’s AI standards database here. To access the standard directly, please visit the developing organisation’s website.

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Tags:

  • System architecture
  • Data collection
  • Data processing
  • Interoperability
  • data sharing

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.