
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.


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enhancing-user-experience-in-interactive-and-personalized-algorithmic-recourse


Enhancing User Experience in Interactive and Personalized Algorithmic Recourse


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Non-adversarial recourse is crucial in high-stakes decision-making scenarios, requiring alignment with ground truth labels.


understanding-non-adversarial-algorithmic-recourse-in-machine-learning


Understanding Non-Adversarial Algorithmic Recourse in Machine Learning
