Race-neutral vs race-conscious: Using algorithmic methods to evaluate the reparative potential of housing programs
Name
so-d-ignazio-2023-race-neutral-vs-race-conscious-using-algorithmic-methods-to-evaluate-the-reparative-potential-of.pdf
Description
Published version
Size
1.5 MB
Format
Adobe PDF
Checksum (MD5)
9feb912a216590de212fd31d1e1c8f55
Author(s)
So, Wonyoung
D’Ignazio, Catherine
Date Issued
July 2023
Journal
Big Data & Society
Publisher
SAGE Publications
Citation
So, W., & D’Ignazio, C. (2023). Race-neutral vs race-conscious: Using algorithmic methods to evaluate the reparative potential of housing programs. Big Data & Society, 10(2).
Version
Final published version
Abstract
The racial wealth gap in the United States remains a persistent issue; white individuals possess six times more wealth than Black individuals. Leading scholars and public figures have pointed to slavery and post-slavery discrimination as root cause factors and called for reparations. Yet the institutionalization of race-neutral ideologies in policies and practices hinders a reparative approach to closing the racial wealth gap. This study models the use of algorithmic methods in the service of reparations to Black Americans in the domain of housing, where most American wealth is built. We examine a hypothetical scenario for measuring the effectiveness of race-conscious Special Purpose Credit Programs (SPCPs) in reducing the housing racial wealth gap compared to race-neutral SPCPs. We use a predictive model to show that race-conscious, people-based lending programs, if they were nationally available, would be two to three times more effective in closing the racial housing wealth gap than other, existing forms of SPCPs. In doing so, we also demonstrate the potential for using algorithms and computational methods to support outcomes aligned with movements for reparations, another possible meaning for the emerging discourse on “algorithmic reparations.”
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/20539517231210272
