Abstract Counterfactual explanations are widely used to explain ML model predictions by providing alternative scenarios. However, choosing the most appropriate explanation method and one of generated counterfactuals is not an easy task. In this paper, we propose an approach that filters out a large set of counterfactuals generated by a set of diverse algorithms through a multi-criteria subset selection problem solved using the dominance relation. Experiments show that exploiting the dominance relation results in a concise set of counterfactual explanations.

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Cite as

@article{stepka2023usefulness,
  title={On usefulness of dominance relation for selecting counterfactuals from the ensemble of explainers},
  author={Stępka, Ignacy and Lango, Mateusz and Stefanowski, Jerzy},
  journal={Proceedings of the 4rd Polish Conference on Artificial Intelligence, PP-RAI 2023},
  year={2023},
  publisher={Wydawnictwo Politechniki Łódzkiej},
  doi={10.34658/9788366741928},
  pages={125-130}
}