Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this workshop, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. Specifically, we plan to facilitate workshop interactions that will shed light onto the following 3 questions: (i) What are the practical, legal and ethical considerations that decision-makers need to account for when providing recourse? (ii) How do humans understand and act based on recourse explanations from a psychological and behavioral perspective? (iii) What are the main technical advances in explainability and causality in ML required for achieving recourse? Our ultimate goal is to foster conversations that will help bridge the gaps arising from the interdisciplinary nature of algorithmic recourse and contribute towards the wider adoption of such methods.