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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ETSI GR ENI 009 V 1.1.1 - Experiential Networked Intelligence (ENI) - Definition of data processing mechanisms

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The present document revises ETSI GR ENI 009. The realization of intelligent network depends on the big data, AI algorithms and computing resources. Therefore, effective data management and operation is extremely important. This work item is purposed to draft a GR of data operation requirements and mechanisms to better serve ENI system: 

  • (1) Data format among the Functional Block of ENI system and towards the external world (internal Functional Blocks, Knowledge Representation), 
  • (2) Data Conversion and possibility to translate AI data model to be adapted to / from external system (external trained model imported into ENI), 
  • (3) Consistency of data format and interface to accelerate the Autonomus Network (AN) evolution process and 
  • (4) Ensure that customer privacy is not disclosed in the entire lifecycle of data collection, processing, and utilization (Federated Learning). 

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Tags:

  • data quality
  • Data collection
  • Data processing
  • 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.