标题： 算法集体行动与多个集体 显示英文标题

标题： Algorithmic Collective Action with Multiple Collectives

摘要： 随着学习系统在日常决策中发挥越来越重要的作用，通过算法集体行动（ACA）协调共享数据的更改，在用户端进行引导，为监管端的政策和企业端的模型设计提供了一种补充。 尽管现实世界中的行动传统上是分散的，并被分割成多个集体，尽管它们有着共同的总体目标——每个集体在规模、策略和可操作目标上有所不同，但大多数ACA文献集中在单一集体的设置上。 在本工作中，我们提出了第一个针对多个集体在同一系统上行动的ACA理论框架。 特别是，我们关注分类中的集体行动，研究多个集体如何植入信号，即让分类器学习特征的修改版本与选定的、可能重叠的目标类别集之间的关联。 我们提供了关于集体规模及其目标对齐在其中的作用和相互作用的定量结果。 我们的框架通过补充先前的实证结果，为对多个集体的ACA进行全面处理开辟了一条路径。 显示英文摘要

摘要： 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.