Algorithmic Collective Action with Multiple Collectives

TL;DR

This paper introduces a theoretical framework for Algorithmic Collective Action involving multiple collectives influencing shared classification systems, analyzing their interactions and effects on biasing classifiers.

Contribution

It is the first to formalize multi-collective ACA, studying their combined impact on classifiers and providing quantitative insights into their size and goal alignment effects.

Findings

Multiple collectives can effectively bias classifiers through coordinated actions.

The size and goal alignment of collectives significantly influence their impact.

The framework complements empirical results and enables holistic analysis of multi-collective ACA.

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.