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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Environmental Sustainability

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Scope

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If you have a tool that you think should be featured in the Catalogue of AI Tools & Metrics, we would love to hear from you!

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This page includes technical metrics and methodologies for measuring and evaluating AI trustworthiness and AI risks. These metrics are often represented through mathematical formulas that assess the technical requirements for achieving trustworthy AI in a particular context. They can help to ensure that a system is fair, accurate, explainable, transparent, robust, safe, or secure.
Objective Environmental Sustainability
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Sparsity 1 related use case

While smoothness and spatial locality capture spatial properties, the individual values shall also be sparse, since few highly important regions are more indicative of a good explanation than several mildly relevant ones. This is why a sparsity metric shoul...

Objectives:

RobustnessEnvironmental Sustainability

FrugalScore

FrugalScore is a reference-based metric for Natural Language Generation (NLG) model evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performan...

Objectives:

Environmental SustainabilityTransparency
Partnership on AI

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.