arXiv:2605.27689v1 Announce Type: new Abstract: When machine learning systems under-perform for particular subgroups, affected users typically have no way to correct these disparities without relying on platform-level fixes. Existing approaches to algorithmic fairness rely on provider-centric approaches to correct these failures, leaving users with no external lever when faced with harm. Recent work in Algorithmic Collective Action shows that coordinated users can steer an algorithmic system toward a collective goal, but the existing mechanisms require the provider to retrain on the collective
This research emerges as AI systems become increasingly pervasive, highlighting the growing tensions between algorithmic governance and user autonomy, particularly around fairness and accountability.
This work introduces a concrete mechanism for users to collectively intervene in algorithmic fairness, shifting power dynamics from platform-centric control towards user-driven correction.
Algorithmic fairness can now be influenced not just by providers but also through coordinated user proxy-based perturbations, offering a new avenue for rectifying AI harms.
- · Affected user subgroups
- · Fairness advocacy groups
- · Ethical AI researchers
- · Platforms with inequitable algorithms
- · Providers unwilling to cede control
- · Traditional platform-centric fairness models
Users gain a tangible, though indirect, mechanism to influence algorithm behavior and fairness.
Platform providers may face increased pressure to integrate user feedback mechanisms or pre-emptively address fairness concerns to avoid collective action.
This could lead to regulatory frameworks mandating such collective action mechanisms, redefining user rights in algorithmic spaces.
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Read at arXiv cs.LG