# Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences

@article{Toni2022GeneratingPC, 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}, journal={ArXiv}, year={2022}, volume={abs/2205.13743} }

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…

## One Citation

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