# A Statistical Framework for Algorithmic Collective Action with Multiple Collectives

arXiv: https://arxiv.org/abs/2605.06749 · HTML: https://mlsanity.com/paper/2605.06749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 13 of 13)

- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023)
- [Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies](https://mlsanity.com/paper/2012.09995/) (2020)
- [(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)
- [Minority Collective Action for User-Side Fairness](https://mlsanity.com/paper/2508.15374/) (2025)
- [Algorithmic Collective Action with Multiple Collectives](https://mlsanity.com/paper/2508.19149/) (2025)
- [Sync or Sink: Bounds on Algorithmic Collective Action with Noise and Multiple Groups](https://mlsanity.com/paper/2510.18933/) (2025)

## Abstract (arXiv)

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. To address this, we propose the first comprehensive statistical 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 influence a classifier's behavior. We provide quantitative statistical bounds on the success of the collectives, considering the role and the interplay of the collectives' sizes and the alignment of their goals. We make such bounds computable by each collective with only partial knowledge of other collectives' sizes and strategies. Finally, we numerically illustrate our framework on simulations inspired by interventions for climate adaptation in smart cities, demonstrating the usefulness of our bounds.