# Algorithmic Collective Action with Multiple Collectives

arXiv: https://arxiv.org/abs/2508.19149 · HTML: https://mlsanity.com/paper/2508.19149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023)
- [(Un)informed Consent: Studying GDPR Consent Notices in the Field](https://mlsanity.com/paper/1909.02638/) (2019)
- [The Role of Learning Algorithms in Collective Action](https://mlsanity.com/paper/2405.06582/) (2024)
- [Decline Now: A Combinatorial Model for Algorithmic Collective Action](https://mlsanity.com/paper/2410.12633/) (2024)
- [Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists](https://mlsanity.com/paper/2404.04269/) (2024)
- [Algorithmic Collective Action with Two Collectives](https://mlsanity.com/paper/2505.00195/) (2025)
- [Statistical Collusion by Collectives on Learning Platforms](https://mlsanity.com/paper/2502.04879/) (2025)
- [Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy](https://mlsanity.com/paper/2505.05707/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Statistical Framework for Algorithmic Collective Action with Multiple Collectives](https://mlsanity.com/paper/2605.06749/) (2026)
- [Sequential Data Poisoning in LLM Post-Training](https://mlsanity.com/paper/2606.04929/) (2026)

## Abstract (arXiv)

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.