ALGORITHMIC COLLECTIVE ACTION

How can collectives understand, contest and shape AI systems? Explore the levers of algorithmic collective action from recommender systems and gig platforms to agentic AI.

Algorithmic Collective Action levers along the AI life cycle.

The AI life cycle offers many points of intervention for society, from data collection and training to deployment and evaluation. This offers new opportunities to extend classical means of collective action like strikes, boycotts, and collective bargaining to the algorithmic sphere and turn the tools of algorithmic power
into tools of collective agency.

About this project

Tracker

The tracker collects empirical evidence and use-cases of algorithmic collective action in the wild. You can explore, search and filter the 126 cases using our taxonomy.

Explore the Tracker →

Taxonomy

We systematically group cases by how collectives acquire knowledge, how they coordinate, why they act, who acts, when, and where. This taxonomy offers a structured way to navigate the broad landscape of algorithmic collective action.

Read our paper →

Contribute

This website is a collective effort: we welcome contributions that show the levers of algorithmic collective action, both in theory and in practice.

Get in touch →

FAQ

Find answers to some common questions about the project.

  • What is the purpose of this project?

    The tracker collects empirical evidence of algorithmic collective action and organizes these cases in a structured taxonomy. This collection of cases serves as a resource for machine learning researchers to ground models and studies of algorithmic collective action in real world evidence, with the goal of bridging between theory and practice. At the same time, the website together with the accompanying workshop paper is designed to provide an easy-to-understand resource for newcomers to discover and navigate the growing field of Algorithmic Collective Action.

  • How did we build this ?

    We collected data during 6 months (starting mid-February 2026) to get an initial database of Algorithmic Collective Actions in the wild. We inductively built the Taxonomy from these cases, and developed the website until its first release in October 2026. For more details on our methodology, you can read our paper.

    We rely on contributions from the community to enlarge the collection of cases. Our heartfelt gratitude goes to our contributors for suggesting cases, ideas or pointing out typos in the website: Simon Zilinskas, Dorothee Sigg, Celestine Mendler-Dünner, Dariia Haryfullina, Simon Chignard.

    Note that the repository is not exhaustive: there might be some cases that we mised in our data collection. The contribution form is here to remedy that. Please help us get a complete list!

  • Is this website AI-generated ?

    Only the website code, not its contents. The collection of real-world cases, their annotation, the writing of their English summaries, and all surrounding work was not AI-generated. It was crafted and verified by humans. We used a coding agent (via OpenCode) for the HTML/CSS/Javascript web development and to help with translation.

  • What is the scope?

    We focus on efforts of algorithmic collective action that aim to steer an AI system by leveraging different intervention points along the AI life cycle. This ranges form data strikes, efforts of data manipulation, and conscious interaction with AI system, to strategic and coordianted user feedback, and efforts of collective sense making. The different facets are explained on our landing page. The focus on algorithmic collective action is a deliberate choice. We aim to highlight levers that are complementary to traditional action happening on the streets, and unique to learning systems. This focus differentiates our work from other efforts such as the AI Resist List, or the Worker mobilizations in Culture tracker by focusing on a specific and complementary avenue for action.

  • How can I contribute?

    We envision this website as a participatory initiative. You are welcome to contribute cases or ideas through our contact form.

    If you found what seems to be an error, feel free to let us know here as well.

Cite this work

@inproceedings{acaRepo2026,
  title     = {The Algorithmic Collective Action Repository},
  author    = {le Douarec, Léo and Haryfullina, Dariia and Mendler-Dünner, Celestine},
  booktitle = {To appear at Workshop on Economics for Machine Learning at NeurIPS 2026},
  year      = {2026},
}