Participatory Personalization in Classification

Machine learning models are often personalized based on 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 information that a model needs to personalize their predictions nor tell if they benefit from personalization in the first place. We introduce a new family of prediction models, called participatory systems, that let individuals opt into personalization at prediction time. We present a model-agnostic algorithm to learn participatory systems for supervised learning tasks where models are personalized with categorical group attributes.