I Prefer not to Say: Are Users Penalized for Protecting Personal Data?

We examine the problem of obtaining fair outcomes for individuals who choose to share optional information with machine-learned models and those who do not consent and keep their data undisclosed. We find that these non-consenting users receive significantly lower prediction outcomes than justified by their provided information alone. This observation gives rise to the overlooked problem of how to ensure that users, who protect their personal data, are not penalized. While statistical fairness notions focus on fair outcomes between advantaged and disadvantaged groups, these fairness notions fail to protect the non-consenting users. To address this problem, we formalize protection requirements for models which (i) allow users to benefit from sharing optional information and (ii) do not penalize them if they keep their data undisclosed. We offer the first solution to this problem by proposing the notion of Optional Feature Fairness (OFF), which we prove to be loss-optimal under our protection requirements (i) and (ii). To learn OFF-compliant models, we devise a model-agnostic data augmentation strategy with finite sample convergence guarantees. Finally, we extensively analyze OFF on a variety of challenging real-world tasks, models, and data sets with multiple optional features.