Impact of Bias on School Admissions and Targeted Interventions

Evaluation criteria for school admissions do not account for the impact of implicit bias on applications, due to e.g., socioeconomic status of students or training resources available to them. We present, to the best of our knowledge, the first mathematical analysis of the impact of biased evaluations of students on school admissions. In our model, students have a unanimous ranking of schools, schools observe "biased" potentials for a group of students and true potentials for others, and then accept the best students from those available to them. Our framework for evaluating the effects of bias and of targeted intervention can incorporate any distribution of students' potentials and deals with the following questions: how much are students and schools affected by such bias; do schools have an incentive to interview students; how should limited resources be allocated to minimize the "impact" of bias on students. When students' potentials are Pareto distributed, we find that schools have little incentive to change their evaluation mechanisms. Moreover, additional resources are best targeted at average students, as opposed to top students, thus questioning existing scholarship mechanisms. This optimal target range shifts towards top students, when students' potentials are Gaussian distributed. We validate these findings using SAT scores data from New York City high schools. Our conclusions are robust, as qualitative takeaways from our analysis remain the same even when some of our modeling assumptions are relaxed.