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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SHAP

Website

SHAP

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

About the tool

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Developing organisation(s):

Objective(s):

Lifecycle stage(s):

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License:

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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

Explainability, defined in alignment with the OECD’s AI principles as “enabling people affected by the outcome of an AI system to understand how it was arrived at,” is one of the five values-focused cross-sectoral principles described in the Science ...
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.