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The Reparative Technology Lab studies urban technologies, collects data, and builds tools and visualizations to expose the inequities behind urban technologies and contribute to repairng the relationships by acknowledging and addressing past harms.
Research Themes
Exposing Inequality in Urban Technology
We investigate urban technologies' process of collecting data and training algorithms that render historical harms invisible during the measurement, training, and deployment processes.
Reparative Technology
We develop actionable and reparative algorithms that have direct implications for racial justice in housing policy.
Community-centered Data Visualization
We develop data visualization tools that are designed to be used by communities to understand and address their own data.
Our Approach
Our approach is uncover how these systems formalize present-day outputs in ways that disconnect them from past wrongs — and to work toward redress.
Our Work
By Research Theme
By Tags
Housing data politics in the United States: Inequitable open data, informal networks, and strategic neutrality
Journal of Urban Affairs
Choice denied: impact of income and credit-based tenant screening on the Housing Choice Voucher program
Housing Studies
Reparative Urban Science: Challenging the Myth of Neutrality and Crafting Data-Driven Narratives
Planning Theory & Practice
Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin
Journal of Urban Affairs
Racial Steering by Large Language Models: A Prospective Audit of GPT-4 on Housing Recommendations
EAAMO '24
Which Information Matters? Measuring Landlord Assessment of Tenant Screening Reports
Housing Policy Debate
Race-neutral vs race-conscious: Using algorithmic methods to evaluate the reparative potential of housing programs
Big Data & Society
The audit: Perils and possibilities for contesting oppression in the heritage landscape
Beyond Fairness: Reparative Algorithms to Address Historical Injustices of Housing Discrimination in the US
FAccT '22
Team
Wonyoung So is an Assistant Professor of Urban Technology at Taubman College of Architecture & Urban Planning, University of Michigan – Ann Arbor.
He works 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, with a particular focus on housing. He combines data-driven methods and data visualization practices to empirically bolster and rhetorically frame narratives for translating scholarly research into public impact. He centers issues of justice and equity to promote the democratic participation of marginalized residents—particularly those marginalized by class, race, gender, and disability, among other durable categories of inequality—who are disproportionately excluded from accessing resources and opportunities, especially when such access is governed by algorithms and AI.
Interested in joining?
We're always looking for passionate researchers and collaborators who share our vision for urban justice and technology. Get in touch to learn more about opportunities.