I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data

We examine machine learning models in a setup where individuals have the choice to share optional personal information with a decision-making system, as seen in modern insurance pricing models. Some users consent to their data being used whereas others object and keep their data undisclosed. In this work, we show that the decision not to share data can be considered as information in itself that should be protected to respect users' privacy. This observation raises the overlooked problem of how …

consent cs.ai cs.cy cs.lg data decision information insurance machine machine learning machine learning models making modern personal data personal information pricing setup stat.ml