Metrics for Trustworthy AI

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

False Acceptance Rate (FAR)

False acceptance rate (FAR) is a security metric used to measure the performance of biometric systems such as voice recognition, fingerprint recognition, face recognition, or iris recognition. It represents the likelihood of a biometric system mistakenly ac...

Objectives:

False Rejection Rate (FRR)

False rejection rate (FRR) is a security metric used to measure the performance of biometric systems such as voice recognition, fingerprint recognition, face recognition, or iris recognition. It represents the likelihood of a biometric system mistakenly rej...

Objectives:

Performance Privacy & data governance Robustness & digital security Safety

Kendall rank correlation coefficient (KRCC)

In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient, is a statistic used to measure the ordinal association between two measured quantities. A τ test is a non-parametric hypothesis test for statistical de...

Objectives:

Fairness Performance Privacy & data governance Robustness & digital security Safety Transparency & explainability

Attack Success Rate (ASR)

The Attack Success Rate (ASR) measures the effectiveness of adversarial attacks against machine learning models. It is calculated as the percentage of attacks that successfully cause a model to misclassify or generate incorrect outputs. Thi...

Objectives:

Robustness & digital security Safety

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.