Towards Reliable and Practicable Algorithmic Recourse

As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in developing post hoc techniques which provide recourse to individuals who have been adversely impacted by predicted outcomes. For example, when an individual is denied loan by a predictive model deployed by a bank, they should be informed about reasons for this decision and what can be done to reverse it. While several approaches have been proposed to tackle the problem of generating recourses, these techniques rely heavily on various restrictive assumptions. For instance, these techniques generate recourses under the assumption that the underlying predictive models do not change. In practice, however, models are often updated for a variety of reasons including data distribution shifts. There is little to no research that systematically investigates and addresses these limitations. In this talk, I will discuss some of our recent work that sheds light on and addresses the aforementioned challenges, thereby paving the way for making algorithmic recourse practicable and reliable. First, I will present theoretical and empirical results which demonstrate that the recourses generated by state-of-the-art approaches are often invalidated due to model updates. Next, I will introduce a novel algorithmic framework based on adversarial training to generate recourses that remain valid even if the underlying models are updated. I will conclude the talk by presenting theoretical and empirical evidence for the efficacy of our solutions, and also discussing other open problems in the burgeoning field of algorithmic recourse.