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.

SHAP



SHAP

A game theoretic approach to explain the output of any machine learning model.

About the tool


Developing organisation(s):



Lifecycle stage(s):




Programming languages:



Github stars:

  • 18389

Github forks:

  • 2777

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Use Cases

Nvidia: Explainable AI for credit risk management

Nvidia: Explainable AI for credit risk management

This case study was published in collaboration with the Centre for Data Ethics and Innovation Portfolio of AI Assurance Techniques. You can read more about the Portfolio and how you can upload your own use case here.Explainability, defined in alignme...
Sep 11, 2023

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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.