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.

Privacy & data governance

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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 Privacy & data governance

The anonymity set for an individual u, denoted ASu is the set of users that the adversary cannot distinguish from u. It can be seen as the size of the crowd into which the target u can blend.


privASS ≡ |ASu |

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The most general time-based metric measures the time until the adversary’s success. It assumes that the adversary will succeed eventually, and is therefore an example of a pessimistic metric. This metric relies on a definition of success, and varies depend...


This metric counts the information items S disclosed by a system, e.g., the number of compromised users. However, this metric does not indicate the severity of a leak because it does not account for the
sensitivity of the leaked information.

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We discuss information-theoretic anonymity metrics, that use entropy over the distribution of all possible recipients to quantify anonymity. We identify a common misconception: the entropy of the distribution describing the potential receivers does not alw...


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...


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...


The structural similarity index measure (SSIM) measures the perceived similarity of two images. When one image is a modified version of the other (e.g., if it is compressed) the SSIM serves as a measure of the fidelity of the compressed representation. The ...


The Fréchet inception distance (FID) typically measures the quality of image generative models. More specifically, FID is a semimetric commonly applied to generative models based on generative adversarial networks (GANs), which was among the first generativ...


The learned perceptual image patch similarity (LPIPS) is used to judge the perceptual similarity between two images. LPIPS is computed with a model that is trained on a labeled dataset of human-judged perceptual similarity. The perception-measuring model co...


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...


Contextual Outlier INterpretation (COIN) is a method designed to explain the abnormality of existing outliers spotted by detectors. The interpretability for an outlier is achieved from three aspects: outlierness score, att that contribute to the abnormality, a...

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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.