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.
Stability
Robustness Metrics provides lightweight modules in order to evaluate the robustness of classification models. Stability is defined as, e.g. the stability of the prediction and predicted probabilities under natural perturbation of the input.
The library includes popular out-of-distribution datasets (ImageNetV2, ImageNet-C, etc.) and can be readily applied to benchmark arbitrary models and is not limited to vision models: any mapping from input -> logits will do.
Related use cases :
Robustness, Stability, Recoverability and Reliability in Dynamic Constraint Satisfaction Problems
Uploaded on Oct 25, 2022Many real-world problems in Artificial Intelligence (AI) as well as in other areas of computer science and engineering can be efficiently modeled and solved using constraint pr...
About the metric
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