During constituency listening processes (CLPs; e.g., town halls), leaders hear directly from community members about their needs and interests, and use these reports to inform high-impact decisions (e.g., resource allocations). While civic leaders work to integrate community preferences into existing civic plans and processes, large volumes of qualitative feedback (e.g., voice recordings, transcripts, survey data) complicates the process of translating voices into effective and sustainable insights and decisions. Within some of these processes, voices can be lost or biases can emerge, leading to decisions that feel out-of-line with community needs. Within others that leverage voices with great care, communicating how voices have informed decision making and what needs have been prioritized and why is quite difficult, often leaving impacted community members feeling left in the dark with little trust in the process.
In this project, we explore how we might transparently and rigorously scaffold the voices-to-decision pipeline of CLPs by designing a database visualization platform, or a “living library” of community insights, that demonstrates how voices impact and align with decisions; that illustrates the shape and meaning of the data for community members and civic leaders; that supports accessible AI-aided analysis of highly complex data; and that scaffolds the critical, broadly impactful, but highly under explored process of going from voice to insights and decision.