Online Algorithmic Recourse by Collective Action
Elliot Creager Richard Zemel
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.