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.

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ITU-T Y.3170 - Requirements for machine learning-based quality of service assurance for the IMT-2020 network

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This Supplement analyses use cases for machine learning in future networks including IMT-2020, and presents them in a unified format. It provides use case descriptions and indicates the basic set of possible requirements for each use case. The use cases are divided into categories. © ITU 2022 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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Tool type(s):

Objective(s):

Target sector(s):

Type of approach:

Maturity:

Usage rights:

Target groups:

Geographical scope:

Tags:

  • System architecture
  • Data collection
  • Data processing
  • Security and resilience
  • Accuracy and performance
  • 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.