Next, I discuss methodologies of reparative urban science. This approach aims to use urban technology to challenge race-neutral ideologies and create data-driven narratives supporting reparations.

I then show how urban science contributes to perpetuating today's racial inequality, particularly by obfuscating efforts to redress the harms of historical oppression. This occurs through three mechanisms: formalization, context removal and legitimization, and penalization and extraction.

In defining urban science, mainly used in academia, I focus on technologies in urban settings sharing the same epistemic umbrella: 1) The belief that cities can be 'modeled' and 2) The idea that outcomes of these models provide 'objective' and 'fair' reasoning.

We found that landlords who serve lower-income renters and smaller landlords used AI-enabled tenant screening the most. One reason for the uptick amongst small landlords could be a lack of resources or legal counsel to develop rental practices complying fair housing laws.

We also found that there is a huge information asymmetry - the survey asked renters to provide the name of the company that conducted their application screening. Only 3% provided the name of a screening or consumer reporting agency.

We found that almost two-thirds of the landlords we surveyed received tenant screening reports that contained some AI-generated score or recommendation, and they often rely heavily on the score alone to make rental decisions.