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.

Robustness Metrics provides lightweight modules in order to evaluate the robustness of classification models. Stability is defined as, e.g. the stability of the prediction and predicted probabilities under natural perturbation of the input.

The library includes popular out-of-distribution datasets (ImageNetV2, ImageNet-C, etc.) and can be readily applied to benchmark arbitrary models and is not limited to vision models: any mapping from input -> logits will do.

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Uploaded on Oct 25, 2022

Many real-world problems in Artificial Intelligence (AI) as well as in other areas of computer science and engineering can be efficiently modeled and solved using constraint pr...



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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.