Towards Reliable and Practicable Algorithmic Recourse
Himabindu Lakkaraju · 2021
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.