# Algorithmic Collective Action with Two Collectives

arXiv: https://arxiv.org/abs/2505.00195 · HTML: https://mlsanity.com/paper/2505.00195/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [The Social Cost of Strategic Classification](https://mlsanity.com/paper/1808.08460/) (2018)
- [Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models](https://mlsanity.com/paper/2310.13828/) (2023)
- [Subpopulation Data Poisoning Attacks](https://mlsanity.com/paper/2006.14026/) (2020)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [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)
- [ML Attack Models: Adversarial Attacks and Data Poisoning Attacks](https://mlsanity.com/paper/2112.02797/) (2021)
- [Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists](https://mlsanity.com/paper/2404.04269/) (2024)
- [#TulsaFlop: A Case Study of Algorithmically-Influenced Collective Action on TikTok](https://mlsanity.com/paper/2012.07716/) (2020)
- [From Data Leverage to Data Co-Ops: An Institutional Model for User Control over Information Access](https://mlsanity.com/paper/2201.10677/) (2022)

## Built on by (most cited first, 7 of 7)

- [Algorithmic Collective Action with Multiple Collectives](https://mlsanity.com/paper/2508.19149/) (2025)
- [With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems](https://mlsanity.com/paper/2603.28476/) (2026)
- [Strategically Deceptive Model Deployment in Performative Prediction](https://mlsanity.com/paper/2506.09044/) (2025)
- [A Human Behavioral Baseline for Collective Governance in Software Projects](https://mlsanity.com/paper/2510.08956/) (2025)
- [A Statistical Framework for Algorithmic Collective Action with Multiple Collectives](https://mlsanity.com/paper/2605.06749/) (2026)
- [Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms](https://mlsanity.com/paper/2605.27689/) (2026)
- [Sequential Data Poisoning in LLM Post-Training](https://mlsanity.com/paper/2606.04929/) (2026)

## Abstract (arXiv)

Given that data-dependent algorithmic systems have become impactful in more domains of life, the need for individuals to promote their own interests and hold algorithms accountable has grown. To have meaningful influence, individuals must band together to engage in collective action. Groups that engage in such algorithmic collective action are likely to vary in size, membership characteristics, and crucially, objectives. In this work, we introduce a first of a kind framework for studying collective action with two or more collectives that strategically behave to manipulate data-driven systems. With more than one collective acting on a system, unexpected interactions may occur. We use this framework to conduct experiments with language model-based classifiers and recommender systems where two collectives each attempt to achieve their own individual objectives. We examine how differing objectives, strategies, sizes, and homogeneity can impact a collective's efficacy. We find that the unintentional interactions between collectives can be quite significant; a collective acting in isolation may be able to achieve their objective (e.g., improve classification outcomes for themselves or promote a particular item), but when a second collective acts simultaneously, the efficacy of the first group drops by as much as 75%. We find that, in the recommender system context, neither fully heterogeneous nor fully homogeneous collectives stand out as most efficacious and that heterogeneity's impact is secondary compared to collective size. Our results signal the need for more transparency in both the underlying algorithmic models and the different behaviors individuals or collectives may take on these systems. This approach also allows collectives to hold algorithmic system developers accountable and provides a framework for people to actively use their own data to promote their own interests.