Testing technique for machine learning and AI models that uses simpler, interpretable surrogate models to analyze the behavior of complex black-box models. It allows for identifying vulnerabilities, biases, and anomalous behaviors by training an approximated model that replicates the target system’s decisions, facilitating robustness and security analysis without direct access to the internal architecture.