  Machine learning models are often personalized with information that is
protected, sensitive, self-reported, or costly to acquire. These models use
information about people but do not facilitate nor inform their consent.
Individuals cannot opt out of reporting personal information to a model, nor
tell if they benefit from personalization in the first place. We introduce a
family of classification models, called participatory systems, that let
individuals opt into personalization at prediction time. We present a
model-agnostic algorithm to learn participatory systems for personalization
with categorical group attributes. We conduct a comprehensive empirical study
of participatory systems in clinical prediction tasks, benchmarking them with
common approaches for personalization and imputation. Our results demonstrate
that participatory systems can facilitate and inform consent while improving
performance and data use across all groups who report personal data.
