Paper-Conference

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.

We propose novel fairness definitions concerning algorithmic recourse. For an individual that receives an undesirable outcome (e.g., loan application rejected), recourse is a way to reverse the outcome (e.g., increase down payment). Recourse incurs a cost for the individual that can be measured quantitatively. Our definitions investigate whether subpopulations have comparable costs, i.e., bear the same burden, for recourse. We have developed a method, termed FACTS, to audit a model for fairness, i.e., find subgroups where unfairness exists.