Ensemble of Counterfactual Explainers
Riccardo Guidotti Salvatore Ruggieri
In eXplainable Artificial Intelligence (XAI), several counterfactual
explainers have been proposed, each focusing on some desirable properties of
counterfactual instances: minimality, actionability, stability, diversity,
plausibility, discriminative power. We propose an ensemble of counterfactual
explainers that boosts weak explainers, which provide only a subset of such
properties, to a powerful method covering all of them. The ensemble runs weak
explainers on a sample of instances and of features, and it combines their
results by exploiting a diversity-driven selection function. The method is
model-agnostic and, through a wrapping approach based on autoencoders, it is
also data-agnostic.