Enhancing User Experience in Interactive and Personalized Algorithmic Recourse

Algorithmic recourse aims to provide actionable suggestions to overturn unfavorable decisions made by automated machine learning models. This work investigates a guided-interaction paradigm that combines preference elicitation with algorithmic recourse to personalize the recourse plans and improve the user experience.