Privacy-Aware Predictions in Participatory Budgeting

Abstract

Participatory budgeting is a democratic innovation that empowers citizens to propose and vote on public investment projects. While researchers in computer science focused on improving the voting phase of this process, in this work we aim to support organizers of participatory budgeting campaigns to manage large volumes of project proposals at the submission stage. We propose a privacy-preserving approach to predict which proposals are likely to be funded, using only projects' textual descriptions and anonymous historical voting records, without relying on voter demographics or personally identifiable information.