You Still See Me: How Data Protection Supports the Architecture of ML Surveillance

Contributor(s)

Rui-Jie Yew, Lucy Qin, Suresh Venkatasubramanian

Session

Dreaming and Design

Abstract

Human data forms the backbone of machine learning. Data protection laws thus have strong bearing on how ML systems are governed. Given that most requirements in data protection laws accompany the processing of personal data, organizations have an incentive to keep their data out of legal scope. This makes the development and application of certain privacy-preserving techniques--data protection techniques--an important strategy for ML compliance. In this paper, we examine the impact of a rhetoric that deems data wrapped in these techniques as data that is "good-to-go". We show how their application in the development of ML systems--from private set intersection as part of dataset curation to homomorphic encryption and federated learning as part of model computation--can further support individual monitoring and data consolidation.