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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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 Safety
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Accuracy 175 related use cases

Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:

Accuracy = (TP + TN) / (TP + TN + FP + FN) , where:

TP: True positive

TN: True negative

FP: False positive

FN...

Objectives:

RobustnessSafetyTransparency

SAFE Artificial Intelligence in finance

We propose a set of interrelated metrics, all based on the notion of AI output concentration, and the related Lorenz curve/Lorenz area under the curve, able to measure the Sustainability/robustness, Accuracy, Fairness/privacy, Explainability/accountability ...

Objectives:

SafetyData Governance & Traceability
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.