Challenging the Myth of Neutrality of Urban Science

Urban science, the harnessing of computational methods and data analysis to address urban planning challenges, is increasingly becoming normative amongst urban planning scholars and practitioners. This widespread adoption suggests that many individuals see more universality in city environments - rather than contextual differences - and their belief that predictions and automated decisions informed by data will be fair and objective. In a paper for Planning Theory and Practice, Wonyoung So offers a critique of this belief, highlighting how urban science can be complicit in perpetuating historical racial inequities and penalizing historically marginalized groups under the guise of race-neutral analysis. 

“In this paper I identify three mechanisms – formalization; context removal and legitimization; and penalization and extraction – that illustrate how urban science, in its current form, is perpetuating historical inequalities,” says So, a doctoral candidate at DUSP. “Building upon my research at MIT and heeding planning scholars’ calls for equity planning, I then offer pathways by which we could change the trajectory of the impact of future urban science research, utilizing new epistemologies and methodologies that can facilitate the establishment of reparative urban science.”

So’s research focuses on examining the role of AI, algorithms, and other data-driven technologies as key mechanisms of housing inequality in the US, particularly as such technologies are deployed in the specific domains of rental housing, eviction, and mortgage lending. His work often interacts at the intersection of urban planning, critical data studies, and data visualization to study how access to resources and opportunities over space and place is mediated by data and technology.

Read So’s full article, Reparative Urban Science: Challenging the Myth of Neutrality and Crafting Data-Driven Narratives.