Auditing for Spatial Fairness

Proceedings 26th International Conference on Extending Database Technology, EDBT 2023, Ioannina, Greece, March 28-31, 2023

In many cases, it is important to ensure that a model does not discriminate against individuals on the basis of their location (place of origin, home address, etc.). We consider location as the protected attribute and we want the algorithm to exhibit spatial fairness For example, consider a model that predicts whether mortgage loan applications are accepted. Its decisions should not discriminate based on the home address of the applicant. This could be to avoid redlining, i.e., indirectly discriminating based on ethnicity/race due to strong correlations between the home address and certain ethnic/racial groups, or to avoid gentrification, e.g., when applications in a poor urban area are systematically rejected to attract wealthier people. We have developed a method to audit a model for spatial fairness, i.e., find areas where spatial unfairness exists.

Discovered areas in the USA where spatial unfairness exists for mortgage loan applications.