A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
This work addresses the challenge for users in explainable AI of selecting among conflicting counterfactual explanations, offering an incremental improvement by automating the selection process.
The paper tackles the problem of selecting the most appropriate counterfactual explanation from multiple methods by proposing a multi-stage ensemble approach that uses multi-criteria analysis to choose a single compromise solution. The experiments showed that this approach generates fully actionable counterfactuals with attractive compromise values on quality measures.
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 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 demonstrated that the proposed approach generates fully actionable counterfactuals with attractive compromise values of the considered quality measures.