  Counterfactual explanations are an increasingly popular form of post hoc
explanation due to their (i) applicability across problem domains, (ii)
proposed legal compliance (e.g., with GDPR), and (iii) reliance on the
contrastive nature of human explanation. Although counterfactual explanations
are normally used to explain individual predictive-instances, 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). These group
counterfactuals meet a human preference for coherent, broad explanations
covering multiple events/instances. A novel, group-counterfactual algorithm is
proposed to generate high-coverage explanations that are faithful to the
to-be-explained model. This explanation strategy is also evaluated in a large,
controlled user study (N=207), using objective (i.e., accuracy) and subjective
(i.e., confidence, explanation satisfaction, and trust) psychological measures.
The results show that group counterfactuals elicit modest but definite
improvements in people's understanding of an AI system. The implications of
these findings for counterfactual methods and for XAI are discussed.
