One of the most significant findings that we can produce when evaluating recourse in machine learning is that a model has assigned a "prediction without recourse."

Predictions without recourse arise when the optimization problem that we solve to search for recourse actions is infeasible. In practice, the "infeasibility" of this problem shows that a person cannot change their prediction through their actions - i.e., that the model has fixed their prediction based on input variables beyond their control.

In this talk, I will discuss these issues and discuss how we can address them by studying the "feasibility" of recourse. First, I will present reasons for why we should ensure the feasibility of recourse -- i.e., even in settings where we may not wish to provide recourse. Next, I will discuss technical challenges that we must overcome to ensure recourse reliably.