About the workshop

About the workshop

Despite opening up unthought opportunities for productivity and commercial development, increasingly capable algorithmic systems have also posed new threats to the well-being of their participants and lead to or exacerbated existing social issues such as biases, labor tensions (Marcin 2019; Wood and Lehdonvirta 2019), and erosion of privacy (Papernot et al. 2018). As a result, those who interact with algorithmic systems have led coordinated campaigns to shape the systems directly or pressure the operators to indirectly implement changes to the systems. Real world examples include the #DeclineNow campaign by Doordash gig workers to increase pay by collectively witholding labor, the use of coordinated “No AI Art” posts by artists on DeviantArt to voice their grievances to the platform, and the coordinated migration of users from X (Twitter) to BlueSky. We refer to (Sigg, Hardt, and Mendler-Dünner 2025) for a living survey of docmumented use cases. These bottom-up efforts demonstrate the organic interests of workers, consumers, and citizens in shaping and pushing back against algorithmic systems.

The study of “collective action” has a long history in Economics and Sociology as a way for groups of people to impact markets and the political arena (Olson 1965; Marwell and Oliver 1993). Algorithmic Collective Action (ACA) is the study of such coordination strategies in algorithmically-mediated sociotechnical systems. Following early explorations in HCI and Computational Social Science (Postmes and Brunsting 2002; Turner et al. 2005; Li et al. 2018), ACA has recently emerged as an exciting new topic for rigorous theoretical and empirical work within Machine Learning (Kulynych et al. 2020; Hardt et al. 2023; Ben-Dov et al. 2024; Baumann and Mendler-Dünner 2024; Gauthier, Bach, and Jordan 2025; Solanki et al. 2025). Initial findings demonstrate how the expertise of the ML community in algorithm design and optimization can be used, not only to build increasingly powerful systems, but also to enable community participation and counter power centralization though the conscious use of data interfaces in AI systems. Moreover, the user-centric perspective of ACA complements the many ongoing efforts of building “fair”, “participatory”, and “responsible” AI systems from within firms.

Our workshop offers a platform to discuss new ideas and help define the foundational research directions for this emerging topic through interdisciplinary discussions between core ML researchers, scholars from the social sciences, community stakeholders and advocates. We will invite speakers and panelists involved in organizing communities to share practical insights and real world challenges, as well as researchers exploring related technical questions within the core ML community. Ultimately our goal is to build a lasting community and promote exciting opportunities for new research, centering the needs of consumers and workers in the study of AI systems.

Speakers

No matching items

Organisers

No matching items

Reviewers

  • Elliot Creager — U of Waterloo
  • Nicholas Vincent — Simon Fraser U
  • Celestine Mendler-Dünner — MPI Tübingen
  • Williiam Agnew — CMU
  • Hanlin Li — U of Texas
  • Ulrich Aïvodji — ETS Montreal
  • Rushabh Solanki — U of Waterloo
  • Meghana Bhange — ETS Montreal
  • Prakhar Ganesh — McGill
  • Khaoula Chehbouni — McGill
  • Parth Sarin — Stanford
  • Afif Taik — Université de Sherbrooke
  • Joachim Baumann — University of Zurich
  • Sara Fish — Harvard
  • Qing Xiao — CMU
  • Etienne Gauthier — Inria
  • Julien Ferry — Polytechnique Montreal
  • Heber Hwang Arcolezi — Inria
  • Patrik Kenfack — ETS Montreal
  • Harry Cheon — UCSD
  • Meredith Stewart — UCSD
  • Shreyas Kadekodi — UCSD
  • Blair Attard-Frost — U of Toronto
No matching items

Reviewer Volunteer Form

If you would like to help as a reviewer, please fill out the form below.

Reviewer self-nomination form

References

Baumann, Joachim, and Celestine Mendler-Dünner. 2024. “Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists.” In The Thirty-Eighth Annual Conference on Neural Information Processing Systems. https://openreview.net/forum?id=wGjSbaMsop.
Ben-Dov, Omri, Jake Fawkes, Samira Samadi, and Amartya Sanyal. 2024. “The Role of Learning Algorithms in Collective Action.” In Proceedings of the 41st International Conference on Machine Learning, edited by Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp, 235:3443–61. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v235/ben-dov24a.html.
Gauthier, Etienne, Francis Bach, and Michael I. Jordan. 2025. “Statistical Collusion by Collectives on Learning Platforms.” https://arxiv.org/abs/2502.04879.
Hardt, Moritz, Eric Mazumdar, Celestine Mendler-Dünner, and Tijana Zrnic. 2023. “Algorithmic Collective Action in Machine Learning.” In Proceedings of the 40th International Conference on Machine Learning. ICML’23. JMLR.org.
Kulynych, Bogdan, Rebekah Overdorf, Carmela Troncoso, and Seda Gürses. 2020. “POTs: Protective Optimization Technologies.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 177–88.
Li, Hanlin, Bodhi Alarcon, Sara Milkes Espinosa, and Brent Hecht. 2018. “Out of Site: Empowering a New Approach to Online Boycotts.” Proceedings of the ACM on Human-Computer Interaction 2 (CSCW): 1–28. https://doi.org/10.1145/3274375.
Marcin, Szczepanski. 2019. “Economic Impacts of Artificial Intelligence.” European Parliamentary Research Service.
Marwell, Gerald, and Pamela Oliver. 1993. The Critical Mass in Collective Action. 1st ed. Cambridge University Press. https://doi.org/10.1017/CBO9780511663765.
Olson, Mancur. 1965. The Logic of Collective Action: Public Goods and the Theory of Groups. 21. printing. Harvard Economic Studies 124. Cambridge, Mass.: Harvard Univ. Press.
Papernot, Nicolas, Patrick McDaniel, Arunesh Sinha, and Michael P. Wellman. 2018. “SoK: Security and Privacy in Machine Learning.” In 2018 IEEE European Symposium on Security and Privacy (EuroS&P), 399–414. https://doi.org/10.1109/EuroSP.2018.00035.
Postmes, Tom, and Suzanne Brunsting. 2002. “Collective Action in the Age of the Internet: Mass Communication and Online Mobilization.” Social Science Computer Review 20 (3): 290–301.
Sigg, Dorothee, Moritz Hardt, and Celestine Mendler-Dünner. 2025. “Decline Now: A Combinatorial Model for Algorithmic Collective Action.” In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–17.
Solanki, Rushabh, Meghana Bhange, Ulrich Aı̈vodji, and Elliot Creager. 2025. “Crowding Out the Noise: Algorithmic Collective Action Under Differential Privacy.” arXiv Preprint arXiv:2505.05707.
Turner, Tammara Combs, Marc A Smith, Danyel Fisher, and Howard T Welser. 2005. “Picturing Usenet: Mapping Computer-Mediated Collective Action.” Journal of Computer-Mediated Communication 10 (4): JCMC1048.
Wood, Alex, and Vili Lehdonvirta. 2019. “Platform Labour and Structured Antagonism: Understanding the Origins of Protest in the Gig Economy.” Available at SSRN 3357804.