Abstract Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions. They can be generated by a variety of methods that optimize different, sometimes conflicting, quality measures and produce quite different solutions. However, choosing the most appropriate explanation method and one of the generated counterfactuals is not an easy task. Instead of forcing the user to test many different explanation methods and analysing conflicting solutions, in this paper, we propose to use a multi-stage ensemble approach that will select single counterfactual based on multiple-critera analysis, in order to offer a compromise solution that scores well on varied quality measures. This approach exploits the dominance relation and the ideal point decision aid method, which selects one counterfactual from the Pareto front. The conducted experiments demonstrated that the proposed approach generates fully actionable counterfactuals with attractive compromise values of the considered quality measures.

Links

• Paper (arxiv, color)
• Paper (official, black&white)

Journal website

• International Journal of Applied Mathematics and Computer Science (AMCS)

Code

• GitHub

Cite as

@article{stepka2024multi,
  title={A Multi--Criteria Approach for Selecting an Explanation from the Set of Counterfactuals Produced by an Ensemble of Explainers},
  author={Stepka, Ignacy and Lango, Mateusz and Stefanowski, Jerzy},
  journal={International Journal of Applied Mathematics and Computer Science},
  volume={34},
  number={1},
  pages={119--133},
  year={2024}
}