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Algorithmic Recourse with Missing Values

Published in arXiv (Cornell University) • Apr 28, 2023
NobleIDNI6P97W01R11S99
Authors:
Kentaro Kanamori
,
T. Takagi
,
Ken Kobayashi

Abstract

This paper proposes a new framework of algorithmic recourse (AR) that works even in the presence of missing values. AR aims to provide a recourse action for altering the undesired prediction result given by a classifier. Existing AR methods assume that we can access complete information on the featu...

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