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.
This work introduces PROMIS, a post-processing optimization framework designed to reduce spatial bias while maintaining predictive performance. Building on threshold-based equal opportunity adjustments and a robust definition of spatial fairness, PROMIS formulates an optimization problem that minimizes a normalized Spatial Bias Index (SBI), which quantifies expected spatial bias across regions. Unlike heuristic correction methods, PROMIS derives globally optimal, interpretable, and computationally efficient fairness adjustments through mathematical optimization, and, unlike white-box approaches, it can be applied to any classification model.
