A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual
  Explanations
André Artelt Andreas Gregoriades
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.