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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Tree Edit Distance (TED)

4 citations of this metric
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Tree Edit Distance (TED) is a metric for calculation of similarity between syntactic n-grams for further detection of soft similarity between texts.

Trustworthy AI Relevance

This metric addresses Robustness and Explainability by quantifying relevant system properties. Robustness: TED quantifies structural differences between model outputs and references (or between in-domain and OOD trees). This makes it useful for detecting distribution shift in tree-structured outputs, measuring consistency under input perturbations, and evaluating resilience to syntactic/noise/adversarial changes in structured prediction tasks.

References

1. Nikulin, M.S., 2001. Hellinger distance. In Encyclopedia of Mathematics. EMS Press. 2. Hellinger, E., 1909. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die reine und angewandte Mathematik, 136, pp.210–271. 3. Jeffreys, H., 1946. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 186(1007), pp.453–461. 4. Liese, F. and Miescke, K.-J., 2008. Statistical Decision Theory: Estimation, Testing, and Selection. Springer. 5. Pardo, L., 2006. Statistical Inference Based on Divergence Measures. Chapman and Hall/CRC.

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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.