A Multi–Criteria Approach for Selecting an Explanation from the Set of Counterfactuals Produced by an Ensemble of Explainers

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