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.
Surrogacy Efficacy Score (SESc)
The Surrogacy Efficacy Score is a technique for gaining a better understanding of the inner workings of complex "black box" models. For example, by using a Tree-based model, this method provides a more interpretable representation of the model’s behavior by partitioning the input data based on the values of certain fields and creating simple rules to approximate the model’s predictions. The Decision Tree model is trained to closely mimic the original model by minimizing the loss between the model’s predictions and surrogate model predictions.
Please refer to the reference website to access the full formula.
About the metric
You can click on the links to see the associated metrics
Objective(s):
Purpose(s):
Lifecycle stage(s):
Target users:
