Fair Feed Ranking for Participatory Budgeting

2026-09-24

AI Summary

This study addresses the problems of agenda manipulation and exposure inequity arising from proposal ranking in participatory budgeting by proposing FairFeed, a fairness-aware ranking framework. FairFeed establishes equitable exposure as a core democratic design objective, integrating declared user preferences with collaborative moderation mechanisms to restructure information feed ranking logic. Specifically, it employs a preference-based weighted ranking algorithm to enhance the visibility of underexposed proposals and introduces a rate-limited veto mechanism to strengthen system resilience against manipulation. Monte Carlo simulation experiments demonstrate that this approach significantly broadens the scope of proposal discovery, achieves a balanced distribution of visibility, and effectively improves cross-domain support rates alongside overall democratic fairness.

Abstract

In large-scale participatory budgeting, citizens cannot inspect the full proposal pool, so the order in which proposals are shown becomes a form of agenda-setting power. We argue that fair exposure should therefore be treated as a democratic-design goal. We study Consul Democracy, a widely deployed open-source digital-democracy platform, and show that its proposal feeds are typically ordered by popularity, recency, or comment activity. Building on this diagnosis, we propose FairFeed, a feed-ranking design for PB that uses transparently declared preferences, boosts under-exposed proposals, and admits a rate-limited reject channel for crowd-sourced vetting. We evaluate the design in a simulation anchored in Munich's 2025 PB process and compare it with random, newest, and most-commented feeds. In this simulation, FairFeed broadens proposal discovery, distributes visibility more evenly across the eligible pool, increases cross-cutting support, and improves resistance to manipulation relative to comment-based ranking. We conclude by outlining the human-subjects evaluation needed to test whether onboarding can recover voter preferences accurately enough for deployment in practice.

Problem

• Participatory Budgeting
• Fair Feed Ranking
• Exposure Fairness
• Digital Democracy
• Agenda-setting

Innovation

• Participatory Budgeting
• Fair Feed Ranking
• Fair Exposure
• Rate-limited Reject Channel
• Manipulation Resistance