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.3174 - Framework for data handling to enable machine learning in future networks including IMT-2020



This Recommendation provides a framework for data handling to enable machine learning (ML) in future networks including International Mobile Telecommunications (IMT)-2020. 
The scope of this Recommendation includes:

  • background and motivations;
  • high-level requirements of data handling and data models (DMs);
  • framework for data handling to enable ML in future networks including IMT-2020;
  • guidelines and examples for usage of the framework in future networks including IMT-2020. 

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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

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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.