Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences

  title={Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences},
  author={G. D. Toni and Paolo Viappiani and Bruno Lepri and Andrea Passerini},
Counterfactual interventions are a powerful tool to explain the decisions of a blackbox decision process, and to enable algorithmic recourse. They are a sequence of actions that, if performed by a user, can overturn an unfavourable decision made by an automated decision system. However, most of the current methods provide interventions without considering the user’s preferences. For example, a user might prefer doing certain actions with respect to others. In this work, we present the first… 

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