Fair Evaluator: An Adversarial Debiasing-based Deep Learning Framework in Student Admissions

This work considers the problem of enhancing the authenticity and fairness of undergraduate student admission decision-making process by employing state-of-the-art Deep Learning (DL) models with advanced bias-mitigation techniques. Traditional admission processes often introduce biases that can disadvantage underrepresented or marginalized groups, highlighting the need for more equitable and efficient methods. Although the DL models have emerged as a promising alternative offering superior performance and higher scalability than the classical Machine Learning approaches, fairness concerns remain a significant issue.We propose an adversarial debiasing-based DL framework that integrates the Optimistic Adam (OAdam) optimizer, ensuring consistent and stable model training crucial for achieving reliable and unbiased outcomes. Our framework leverages data from applicants to the Computer Science Department at the University of California, Irvine. To ensure holistic evaluation of applicants’ profile we utilize a dataset that encompasses a wide range of features showcasing demographics, academic records, high school information, and essay responses. By prioritizing the recall score alongside the fairness metrics, our approach effectively handles the fairness-accuracy trade-off, considerably minimizing the false negatives and ensuring equitable consideration for marginalized groups in admission decisions. Through rigorous experimentation and analysis, our comprehensive study demonstrates that the proposed fairness-aware Input Convex Neural Network model using OAdam optimizer, achieves high fairness metrics while ensuring a balanced predictive performance. The proposed model improves the p-% rule scores by an average of 39.989% across sensitive attributes and achieves recall scores 0.97% higher than those of unfair baseline models.