Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI

Greta Warren, Eoin Delaney, Christophe Guéret, Mark T. Keane

Abstract: Counterfactual explanations have become a major focus for post-hoc explainability research in recent years, as they seem to provide good algorithmic recourse solutions, people can readily understand them, and they may meet legal regulations (such as GDPR in the EU). However, this large literature has only addressed the use of counterfactual explanations to explain single predictive-instances. Here, we explore a novel use case in which groups of similar instances are explained in a collective fashion using “group counterfactuals” (e.g., to highlight a repeating pattern of illness in a group of patients). Group counterfactuals potentially provide broad explanations covering multiple events/instances. A novel case-based, group-counterfactual algorithm is proposed to generate such explanations and a user study is also reported to test the psychological validity of the algorithm.