Shapley Additive Explanation (SHAP)

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.

Overview Tools Metrics About the catalogue

Website

SHAP (SHapley Additive exPlanations) assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties.

About the metric

You can click on the links to see the associated metrics

Objective(s):

Fairness
Performance

Purpose(s):

Reasoning with knowledge structures/planning

Lifecycle stage(s):

Build & interpret model

Target users:

Developer
System operators

Risk management stage(s):

Define

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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.