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Bibliographic Details
Main Authors: Creager, Elliot, Zemel, Richard
Format: Preprint
Published: 2023
Subjects:
Online Access:https://arxiv.org/abs/2401.00055
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author Creager, Elliot
Zemel, Richard
author_facet Creager, Elliot
Zemel, Richard
contents Research on algorithmic recourse typically considers how an individual can reasonably change an unfavorable automated decision when interacting with a fixed decision-making system. This paper focuses instead on the online setting, where system parameters are updated dynamically according to interactions with data subjects. Beyond the typical individual-level recourse, the online setting opens up new ways for groups to shape system decisions by leveraging the parameter update rule. We show empirically that recourse can be improved when users coordinate by jointly computing their feature perturbations, underscoring the importance of collective action in mitigating adverse automated decisions.
format Preprint
id arxiv_https___arxiv_org_abs_2401_00055
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Online Algorithmic Recourse by Collective Action
Creager, Elliot
Zemel, Richard
Machine Learning
Research on algorithmic recourse typically considers how an individual can reasonably change an unfavorable automated decision when interacting with a fixed decision-making system. This paper focuses instead on the online setting, where system parameters are updated dynamically according to interactions with data subjects. Beyond the typical individual-level recourse, the online setting opens up new ways for groups to shape system decisions by leveraging the parameter update rule. We show empirically that recourse can be improved when users coordinate by jointly computing their feature perturbations, underscoring the importance of collective action in mitigating adverse automated decisions.
title Online Algorithmic Recourse by Collective Action
topic Machine Learning
url https://arxiv.org/abs/2401.00055