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.

翻译：反事实解释因其（i）跨问题领域的适用性，（ii）法律合规性（如符合GDPR要求），以及（iii）依托人类解释的对比特性，正成为一种日益流行的事后解释形式。尽管反事实解释通常用于解释单个预测实例，我们探索了一个新颖用例——通过“群体反事实”对相似实例组进行集体解释（例如，突出患者群体中反复出现的疾病模式）。这些群体反事实满足了人类对连贯、广泛且覆盖多个事件/实例的解释偏好。本文提出了一种新型群体反事实算法，用于生成高覆盖率的解释，且这些解释忠实于待解释模型。我们通过大规模受控用户研究（N=207），采用客观（准确性）与主观（信心、解释满意度与信任度）心理测量指标，对该解释策略进行了评估。结果表明，群体反事实能适度但显著地提升人们对AI系统的理解。本文讨论了这些发现对反事实方法及XAI领域的启示。