AI RESEARCH
A Statistical Framework for Algorithmic Collective Action with Multiple Collectives
arXiv CS.AI
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ArXi:2605.06749v1 Announce Type: cross As learning systems increasingly shape everyday decisions, Algorithmic Collective Action (ACA), i.e., users coordinating changes to shared data to steer model behavior, offers a complement to regulator-side policy and corporate model design. Real-world collective actions have traditionally been decentralized and fragmented into multiple collectives, despite sharing overarching objectives, with each collective differing in size, strategy, and actionable goals. However, most of the ACA literature focuses on single collective settings.