Ensemble of Counterfactual Explainers
This addresses the need for comprehensive and robust explainability in AI systems, though it appears incremental as it builds on existing explainers.
The paper tackles the problem of generating counterfactual explanations in XAI by proposing an ensemble method that combines weak explainers to achieve all desirable properties, such as minimality and plausibility, resulting in a model- and data-agnostic approach.
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.