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Paper details
Number 1 - March 2024
Volume 34 - 2024
A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
Ignacy Stepka, Mateusz Lango, Jerzy Stefanowski
Abstract
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining more
desired predictions. They can be generated by a variety of methods that optimize various, 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 a single
counterfactual based on the multiple-criteria analysis. It offers a compromise solution that scores well on several popular
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 demonstrate that the proposed approach generates fully
actionable counterfactuals with attractive compromise values of the quality measures considered.
Keywords
counterfactual explanations, ensemble of explainers, ideal point method, multiple criteria analysis, explainable artificial intelligence