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.
Kendall rank correlation coefficient (KRCC)
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient, is a statistic used to measure the ordinal association between two measured quantities. A τ test is a non-parametric hypothesis test for statistical dependence based on the τ coefficient. It is a measure of rank correlation: the similarity of the orderings of the data when ranked by each of the quantities.
KRCC can support Explainability by providing a quantitative measure of how closely an AI system's outputs match expected or human-generated rankings. This can help users and developers understand whether the AI's decision process aligns with human reasoning or established benchmarks, thereby contributing to more understandable and interpretable AI outputs. However, this connection is indirect and context-dependent, as KRCC does not itself generate explanations but rather evaluates agreement in rankings.
About the metric
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