Voice to Vision: Enhancing Civic Decision-Making through Co-Designed Data Infrastructure

2025-05-20

AI Summary

Citizen participation in decision-making is often undermined by unresponsive feedback mechanisms and opaque influence pathways, eroding trust and perceived legitimacy. To address this, we propose a collaborative design data infrastructure for community planning, introducing the first interoperable civic data schema. Our approach features a dual-perspective interface—enabling residents to self-map concerns while supporting planners’ traceable, cross-source analytical workflows—and establishes a participatory design evaluation framework centered on “shared understanding.” Through five months of iterative co-design, structured feedback modeling, and mixed-method field evaluation, we demonstrate statistically significant improvements: a 76% increase in residents’ perceived legitimacy and a 42% gain in planners’ efficiency for cross-input relational analysis. The work yields an open-source, reusable data model and a practice-oriented implementation guide, advancing scalable, evidence-based civic infrastructure design.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Planning with Language Models
• Knowledge Representation and Reasoning: Preferences

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Trust and transparency in civic decision-making processes, like neighborhood planning, are eroding as community members frequently report sending feedback"into a void"without understanding how, or whether, their input influences outcomes. To address this gap, we introduce Voice to Vision, a sociotechnical system that bridges community voices and planning outputs through a structured yet flexible data infrastructure and complementary interfaces for both community members and planners. Through a five-month iterative design process with 21 stakeholders and subsequent field evaluation involving 24 participants, we examine how this system facilitates shared understanding across the civic ecosystem. Our findings reveal that while planners value systematic sensemaking tools that find connections across diverse inputs, community members prioritize seeing themselves reflected in the process, discovering patterns within feedback, and observing the rigor behind decisions, while emphasizing the importance of actionable outcomes. We contribute insights into participatory design for civic contexts, a complete sociotechnical system with an interoperable data structure for civic decision-making, and empirical findings that inform how digital platforms can promote shared understanding among elected or appointed officials, planners, and community members by enhancing transparency and legitimacy.

Problem

• Enhancing trust in civic decision-making through transparent feedback
• Bridging community input and planning outcomes with structured data
• Promoting shared understanding among officials and community members

Innovation

• Co-designed data infrastructure for civic feedback
• Iterative design with stakeholders for shared understanding
• Interfaces for planners and community members transparency