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.

Normalized Mutual Information is a metric calculated between two clusterings and is a normalization of the Mutual Information (MI) score to scale the results between 0 (no mutual information) and 1 (perfect correlation).

NMI can support Data Governance & Traceability by enabling the comparison and validation of clustering results during data processing or model auditing. This helps ensure that data transformations or clustering steps are consistent and traceable, which is important for oversight and accountability in AI systems. However, this connection is indirect and context-dependent, as NMI itself does not provide governance or traceability but can be a tool within such processes.

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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.