Community-driven data science practices

TL;DR

This paper explores community-driven data science practices through case studies of partnerships in environmental justice and local policing data analysis, emphasizing collaboration, scaling, and social impact.

Contribution

It introduces a framework for developing effective community-research partnerships in data science for social justice, illustrated through two detailed case studies.

Findings

Community partnerships enhance research relevance and impact.

Framework supports scaling local projects to broader contexts.

Collaborative approaches align research with community needs.

Abstract

Mathematics researchers are becoming more involved with research questions at the interface of data science and social justice. This type of research needs to be grounded in the needs of the community in order to have significant impact. In this paper, we examine two examples of community-research partnerships in data science for social justice co-authored by both community members and mathematical researchers. The first, VECINA, is a place-based community-research partnership focused on environmental justice. VECINA introduces a framework for developing fruitful local collaborations. The second example, SToPA, originates in citizens' request for an analysis of their town's policing data, but focuses on how to scale this work beyond that place-based setting. SToPA's research helps us imagine how we can continue to actively collaborate with community members even when working to scale projects beyond a single community. In both of these case studies, we examine the harmonies between established principles of power, process, and perspective with our framework for research-community partnerships. We use a duoethnography approach, directly illustrating the experiences of researchers. We also offer a set of reflections on the impact of these research-community partnerships.