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.

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 always decrease given more information. We show the relation of these a-posteriori distributions with the Shannon conditional entropy, which is an average over all possible observations.

Related use cases :

Uploaded on Oct 25, 2022

The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way...



About the metric



Lifecycle stage(s):



Risk management stage(s):

Modify this metric

catalogue Logos

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.