Systemic Fairness and College Admissions

The increasing volume and complexity of applications to some institutions may make artificial intelligence (AI) a useful tool in college admission process. However, the integration of AI also raises concerns about fairness, particularly across institutions with varying selectivity. Using agent-based simulations grounded in real-world data, this study examines how AI sophistication, institutional selection criteria, opportunity constraints (e.g., fewer universities), and application constraints driven by socioeconomic barriers affect systemic disparities. We extend existing fairness metrics of demographic parity and equal opportunity to the system level and introduce a new metric, choice parity. Results show that selection criteria homogeneity and differences in AI sophistication can amplify inequities, especially when underrepresented students face constrained application options. Although primarily simulation based, this study highlights issues and considerations for policy makers regarding how institutional factors and AI use in the admissions process can potentially impact fairness at a systemic level and offers some policy guidance in this context. History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0523 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0523 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .