Local explanation of black box model based on constrained perturbation and ensemble-based surrogate model

Abstract

Perturbed data generation for explainable Artificial Intelligence (AI) is still an evolving field and attempts are made towards to addressing the technical challenge of correlation of features that degrades generated explanations for block box models in Machine Learning (ML) or AI domain. A method and system for local explanation of black box model based on constrained perturbation and ensemble-based surrogate model is disclosed. The method disclosed averts data correlation problem by performing data perturbation around the local instance in accordance with distribution of test data set and primarily ensures the values of input features associated with the local instance stay within the feature space and does not form out of distribution scenarios (add adversary cheating). The method autogenerates labels for the perturbed data to fit or train an ensemble based surrogate model that eliminates data bias and improves accuracy of generated explanations.