Robust Algorithmic Recourse Design Under Model Shifts
Abstract: Algorithmic recourse offers users recommendations for actions that can help alter unfavorable outcomes in practical decision-making systems. Although many methods have been proposed to design easily implementable recourses, model updates or shifts may render previously generated recourses invalid. This paper addresses this challenge with two key contributions: 1) We introduce an uncertainty quantification method that calculates a theoretical upper-bound for the recourse invalidation rate. 2) We introduce a framework that allows users to manage the trade-off between the implementation cost of recourses and their robustness. This framework leverages the proposed invalidation rate bounds to generate recourses, catering to user-defined robustness requirements.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Bo_Dai1
Submission Number: 2052
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