AI Insight
This study develops and tests algorithmic methods to help organizers shortlist projects in participatory budgeting systems, where citizens propose and vote on public investments. The researchers found that a naive approach using large language models to rank projects performed poorly, but a vote-based pipeline combining LLMs with historical voting data achieved performance comparable to classical machine learning approaches. The results suggest that citizen preferences in participatory budgeting are sufficiently stable to enable effective algorithmic pre-selection of projects while maintaining privacy protections.
Why it matters
As participatory budgeting initiatives scale up and receive increasing numbers of project submissions, automated shortlisting could reduce administrative burden while maintaining democratic integrity. These privacy-preserving algorithms could help municipalities and organizations manage citizen participation more efficiently without compromising the democratic process.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Participatory budgeting is a democratic innovation that allows citizens to propose and vote on public investment projects. To help organizers manage large volumes of submissions, we design and test privacy-preserving methods for algorithmic shortlisting. These algorithms predict which projects are likely to be funded using only project features and anonymous historical voting data. We demonstrate the limitations of a naive approach that uses a large language model to rank projects based on past success and propose a vote-based pipeline that enables state-of-the-art LLMs to perform on par with classical machine learning. Our findings indicate that user preferences in participatory budgeting are stable enough to allow algorithmic shortlisting to approximate an initial selection of projects effectively.