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 significantly increases human satisfaction.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Dennis_Wei1
Submission Number: 3381
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