Race-Neutral vs Race-Conscious: Using Algorithmic Methods to Evaluate the Reparative Potential of Housing Programs
The racial wealth gap in the US 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 a method for 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 Credit Purpose 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 2 to 3 times more effective in closing the racial housing wealth gap than other, existing forms of SPCPs. In so doing, 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.”
This work has been accepted with minor revisions to Big Data & Society.