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Counterfactual explanations constitute among the most popular methods for analyzing the predictions of black-box systems since they can recommend cost-efficient and actionable changes to the input to turn an undesired system's output into a desired output. While most of the existing counterfactual methods explain a single instance, several real-world use cases, such as customer satisfaction, require the identification of a single counterfactual that can satisfy multiple instances (e.g. customers) simultaneously. In this work, we propose a flexible two-stage algorithm for finding groups of instances along with cost-efficient multi-instance counterfactual explanations. This is motivated by the fact that in most previous works the aspect of finding such groups is not addressed.
Artelt et al. (Sat,) studied this question.