Ensemble of Counterfactual Explainers

• URL: http://arxiv.org/abs/2308.15194v1
• Date: Tue, 29 Aug 2023 10:21:50 GMT
• Title: Ensemble of Counterfactual Explainers
• Authors: Riccardo Guidotti, Salvatore Ruggieri
• Abstract summary: We propose an ensemble of counterfactual explainers that boosts weak explainers, which provide only a subset of such properties. 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.
• Score: 17.88531216690148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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