Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
This addresses the problem of balancing privacy protections with user empowerment in AI systems, particularly for everyday users seeking to steer AI behavior, though it is incremental in analyzing interactions with existing privacy methods.
The paper investigates how differential privacy, specifically Differentially Private Stochastic Gradient Descent (DPSGD), impacts the effectiveness of algorithmic collective action by users to influence AI models, finding that it introduces challenges and characterizing lower bounds on success based on collective size and privacy parameters, with experimental verification on deep neural networks.
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 the responsible deployment of trustworthy AI systems is a worthy goal, there are many possible ways to realize it, from policy and regulation to improved algorithm design and evaluation. In fact, since AI trains on social data, there is even a possibility for everyday users, citizens, or workers to directly steer its behavior through Algorithmic Collective Action, by deliberately modifying the data they share with a platform to drive its learning process in their favor. This paper considers how these grassroots efforts to influence AI interact with methods already used by AI firms and governments to improve model trustworthiness. In particular, we focus on the setting where the AI firm deploys a differentially private model, motivated by the growing regulatory focus on privacy and data protection. 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 algorithmic collective action under differential privacy as a function of the collective's size and the firm's privacy parameters, and verify these trends experimentally by simulating collective action during the training of deep neural network classifiers across several datasets.