Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy

The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algorithmic harms and re-entrenching existing social inequities. While top-down solutions such as regulatory policies and improved algorithm design are common, the fact that AI trains on social data creates an opportunity for a grassroots approach, Algorithmic Collective Action, where users deliberately modify the data they share to steer a platform's learning process in their favor. This paper considers how these efforts interact with a firm's use of a differentially private model to protect user data, motivated by the growing regulatory focus on privacy and data protection. In particular, we investigate how the use of Differentially Private Stochastic Gradient Descent (DPSGD) affects the collective’s ability to influence the learning process. Our findings show that while differential privacy contributes to the protection of individual data, it introduces challenges for effective algorithmic collective action. We characterize lower bounds on the success of these actions as a function of the collective's size and the firm's privacy parameters, verifying these trends experimentally by training deep neural network classifiers across several datasets.