Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts

Understand

As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing emphasis on designing algorithms that can provide recourse to affected individuals.

• Existing recourse algorithms function under the assumption that the underlying predictive model does not change.
• However, models are regularly updated in practice for several reasons including data distribution shifts.
• In this work, we make the first attempt at understanding how model updates resulting from data distribution shifts impact the algorithmic recourses generated by state-of-the-art algorithms.