HARE: Human-in-the-Loop Algorithmic Recourse

Abstract

Machine learning models are seeing increasing use as decision making systems in domains such as education, finance and healthcare. It is desirable that these models are trustworthy to the end-user, by ensuring fairness, transparency and reliability of decisions. In this work, we consider a key aspect of responsible and transparent AI models -- actionable explanations, viz. the ability of such models to provide recourse to end users adversely affected by their decisions. While algorithmic recourse has seen a variety of efforts in recent years, there have been very few efforts on exploring personalized recourse for a given user. Two users with the same feature profile may prefer vastly different recourses. The limited work in this direction hitherto rely on one-time feature preferences provided by a user. Instead, we present a human-in-the-loop formulation of algorithmic recourse that can incorporate both relative and absolute human feedback for a given test instance. We show that our formulation can extend any existing recourse generating method, enabling the generation of recourses that are satisfactory to the user. We perform experiments on 3 benchmark datasets on top of 6 popular baseline recourse methods where we observe that our framework performs significantly better on simulated user preferences.