Participatory Personalization in Classification

TL;DR

This paper introduces participatory classification models that enable individuals to opt into personalization, enhancing consent, data use, and performance in clinical prediction tasks.

Contribution

It proposes a model-agnostic algorithm for participatory systems and demonstrates their effectiveness through comprehensive empirical evaluation.

Findings

Participatory systems improve model performance across all groups.

They facilitate informed consent and data reporting.

Empirical results show better data use and personalization benefits.

Abstract

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.