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Trustworthy AI

Here are topics relevant to designing, developing and deploying AI systems that are trustworthy. This thematic overview will cover the characteristics of trustworthy AI systems and other governance measures to ensure that AI is built responsibly.

Highlights

Watch the recording of the Introduction to Trustworthy AI from the Hub’s launch event, go the forums to discuss what trustworthy AI means and see ISO’s 2020 standard on trustworthiness in AI systems.

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Published standards

This work item describes the challenges of securing AI-based systems and solutions, including challenges relating to data, algorithms and models…
Published
Last Updated: 18 Jul 2024

Standards in development

This guide defines a machine learning framework that allows a computing task to be decomposed and distributed across edge and cloud nodes. This guide provides a blueprint for data usage, model learning, and computing collaboration in edge computing environments while…
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Pre-draft
Standards Body: IEEE
Last updated: 12 Jun 2025
This recommended practice provides a framework for the use of large-scale pre-trained deep learning models, including approaches, taxonomies, related roles, and activities, software toolkits, application ability key performance indicators as well as assessment means. Ā© Copyright 2024 IEEE – All…
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Pre-draft
Standards Body: IEEE
Last updated: 12 Jun 2025
This guide provides data contribution measurement methods for federated machine learning. The document provides guidance with respect to data trading feasibility for federated machine learning. The guide describes three main aspects: 1) Framework for data contribution evaluation of federated machine…
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Pre-draft
Standards Body: IEEE
Last updated: 12 Jun 2025