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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IEEE 3301-2022 - Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Artificial Intelligence Framework (AIF) Version 1

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The MPAI AI Framework (MPAI-AIF) Technical Specification specifies architecture, interfaces, protocols and Application Programming Interfaces (API) of an AI Framework (AIF), especially designed for execution of AI-based implementations, but also suitable for mixed AI and traditional data processing workflows. 
MPAI-AIF possesses the following main features:

  • Operating System-independent.
  • Component-based modular architecture with standard interfaces.
  • Interfaces encapsulate Components to abstract them from the development environment.
  • Interface with the MPAI Store enables access to validated Components.
  • Component can be implemented as: software only (from Micro-Controller Units to High-Performance Computing), hardware only, and hybrid hardware-software.

Component system features are:

  • Execution in local and distributed Zero-Trust architectures.
  • Possibility to interact with other Implementations operating in proximity.
  • Direct support to Machine Learning functionalities. 

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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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  • System architecture
  • 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.