Related papers: Algorithmic Collective Action with Multiple Collectives

Algorithmic Collective Action with Multiple Collectives

URL: http://arxiv.org/abs/2508.19149v1
Date: Tue, 26 Aug 2025 16:00:08 GMT
Title: Algorithmic Collective Action with Multiple Collectives
Authors: Claudio Battiloro, Pietro Greiner, Bret Nestor, Oumaima Amezgar, Francesca Dominici,
Abstract summary: Algorithmic Collective Action (ACA)-coordinated changes to shared data-offers a complement to regulator-side policy and firm-side model design.<n>Most ACA literature focused on single collective settings.<n>We present the first theoretical framework for ACA with multiple collectives acting on the same system.
Score: 10.953526453057579
License: http://creativecommons.org/licenses/by/4.0/
Abstract: As learning systems increasingly influence everyday decisions, user-side steering via Algorithmic Collective Action (ACA)-coordinated changes to shared data-offers a complement to regulator-side policy and firm-side model design. Although real-world actions have been traditionally decentralized and fragmented into multiple collectives despite sharing overarching objectives-with each collective differing in size, strategy, and actionable goals, most of the ACA literature focused on single collective settings. In this work, we present the first theoretical framework for ACA with multiple collectives acting on the same system. In particular, we focus on collective action in classification, studying how multiple collectives can plant signals, i.e., bias a classifier to learn an association between an altered version of the features and a chosen, possibly overlapping, set of target classes. We provide quantitative results about the role and the interplay of collectives' sizes and their alignment of goals. Our framework, by also complementing previous empirical results, opens a path for a holistic treatment of ACA with multiple collectives.

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