Privacy-Aware Predictions in Participatory Budgeting

TL;DR

This paper introduces a privacy-preserving predictive method for participatory budgeting that uses only project descriptions and anonymous voting data to forecast funding outcomes, safeguarding voter privacy.

Contribution

It presents a novel approach that predicts funded proposals without using personal voter data, enhancing privacy in participatory budgeting processes.

Findings

Effective prediction of funded proposals using textual and anonymous voting data.

Preserves voter privacy by avoiding use of personally identifiable information.

Applicable to large-scale participatory budgeting campaigns.

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.