Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse

Machine learning models are often used to automate or support decisions in applications such as lending and hiring. In such settings, consumer protection rules mandate that we provide a list of "principal reasons" to consumers who receive adverse decisions. In practice, lenders and employers identify principal reasons by returning the top-scoring features from a feature attribution method. In this work, we study how such practices align with one of the underlying goals of consumer protection - recourse - i.e., educating individuals on how they can attain a desired outcome. We show that standard attribution methods can mislead individuals by highlighting reasons without recourse - i.e., by presenting consumers with features that cannot be changed to achieve recourse. We propose to address these issues by scoring features on the basis of responsiveness - i.e., the probability that an individual can attain a desired outcome by changing a specific feature. We develop efficient methods to compute responsiveness scores for any model and any dataset under complex actionability constraints. We present an extensive empirical study on the responsiveness of explanations in lending and demonstrate how responsiveness scores can be used to construct feature-highlighting explanations that lead to recourse and mitigate harm by flagging instances with fixed predictions.