Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach
This work addresses scalability and bias issues in undergraduate admissions for universities, though it is incremental as it applies existing deep learning and interpretability techniques to a specific domain.
The authors tackled the challenge of validating undergraduate admission decisions by developing deep learning classifiers that achieve 3.03% higher accuracy than traditional machine learning methods, while incorporating interpretability to analyze feature impacts.
This article addresses the challenge of validating the admission committee's decisions for undergraduate admissions. In recent years, the traditional review process has struggled to handle the overwhelmingly large amount of applicants' data. Moreover, this traditional assessment often leads to human bias, which might result in discrimination among applicants. Although classical machine learning-based approaches exist that aim to verify the quantitative assessment made by the application reviewers, these methods lack scalability and suffer from performance issues when a large volume of data is in place. In this context, we propose deep learning-based classifiers, namely Feed-Forward and Input Convex neural networks, which overcome the challenges faced by the existing methods. Furthermore, we give additional insights into our model by incorporating an interpretability module, namely LIME. Our training and test datasets comprise applicants' data with a wide range of variables and information. Our models achieve higher accuracy compared to the best-performing traditional machine learning-based approach by a considerable margin of 3.03\%. Additionally, we show the sensitivity of different features and their relative impacts on the overall admission decision using the LIME technique.