Oracle Corporation
USING GENERATIVE ADVERSARIAL NETWORKS TO CONSTRUCT REALISTIC COUNTERFACTUAL EXPLANATIONS FOR MACHINE LEARNING MODELS

Abstract:

Herein are counterfactual explanations of machine learning (ML) inferencing provided by generative adversarial networks (GANs) that ensure realistic counterfactuals and use latent spaces to optimize perturbations. In an embodiment, a first computer trains a generator model in a GAN. A same or second computer hosts a classifier model that inferences an original label for original feature values respectively for many features. Runtime ML explainability (MLX) occurs on the first or second or a third computer as follows. The generator model from the GAN generates a sequence of revised feature values that are based on noise. The noise is iteratively optimized based on a distance between the original feature values and current revised feature values in the sequence of revised feature values. The classifier model inferences a current label respectively for each counterfactual in the sequence of revised feature values. Satisfactory discovered counterfactuals are promoted as explanations of behavior of the classifier model.